System to determine a change in weight at a load cell

ABSTRACT

Various sensors may be used to gather information about items at an inventory location, such as items on a shelf. Weight sensors may be used to gather weight data while capacitive sensors detect the presence of one or more objects such as the items themselves, a user reaching towards the shelf, and so forth. Data from a capacitive sensor may be used to determine that an activity is taking place at the shelf and to trigger processing of weight data from the weight sensor. A combination of a change in capacitance that exceeds a capacitance threshold and a change in weight that exceeds a weight threshold may be used to determine an event, such as a pick or place from the inventory location.

BACKGROUND

Retailers, wholesalers, and other product distributors typicallymaintain an inventory of various items that may be ordered, purchased,leased, borrowed, rented, viewed, and so forth, by clients or customers.For example, an e-commerce website may maintain inventory in afulfillment center. When a customer orders an item, the item is pickedfrom inventory, routed to a packing station, packed, and shipped to thecustomer. Likewise, physical stores maintain inventory in customeraccessible areas, such as in a shopping area, and customers can pickitems from inventory and take them to a cashier for purchase, rental,and so forth.

Many physical stores also maintain inventory in a storage area,fulfillment center, or other facility that can be used to replenishinventory located in the shopping areas or to satisfy orders for itemsthat are placed through other channels (e.g., e-commerce). Otherexamples of entities that maintain facilities holding inventory includelibraries, museums, rental centers, and so forth. In each instance, foran item to be moved from one location to another, it is picked from itscurrent location and transitioned to a new location. It is oftendesirable to monitor quantity or movement of inventory within thefacility.

BRIEF DESCRIPTION OF FIGURES

The detailed description is set forth with reference to the accompanyingfigures. In the figures, the left-most digit(s) of a reference numberidentifies the figure in which the reference number first appears. Theuse of the same reference numbers in different figures indicates similaror identical items or features. The figures are not necessarily drawn toscale, and in some figures, the proportions or other aspects may beexaggerated to facilitate comprehension of particular aspects.

FIG. 1 illustrates a system using sensors such as capacitive sensors andweight sensors to generate interaction data about an inventory location,according to some implementations.

FIG. 2 illustrates the arrangement of conductive elements used forcapacitive sensing and weight sensors at an inventory location,according to some implementations.

FIG. 3 illustrates several layouts of arrays of conductive elements usedfor capacitive sensing, according to some implementations.

FIG. 4 illustrates a block diagram of using a seed value to generate asequence of operation of conductive elements in capacitive sensors tominimize interference with other capacitive sensors, according to someimplementations.

FIG. 5 illustrates a block diagram of conductive elements connected viaa switch module to a capacitive sensor module or antenna matchingnetwork, according to some implementations.

FIG. 6 illustrates a block diagram of an analysis module generatinginteraction data, according to some implementations.

FIG. 7 is a block diagram illustrating a materials handling facility(facility) using the system, according to some implementations.

FIG. 8 is a block diagram illustrating additional details of thefacility, according to some implementations.

FIG. 9 is a block diagram of a server to support operation of thefacility, according to some implementations.

FIG. 10 depicts a flow diagram of a process for determining a locationof an interaction at an inventory location, according to someimplementations.

FIG. 11 depicts a flow diagram of another process for determining alocation of an interaction at an inventory location, according to someimplementations.

FIG. 12 depicts a flow diagram of a process for generating and operatingconductive elements in a capacitive sensor, according to someimplementations.

FIG. 13 depicts a flow diagram of a process for using different seedvalues in adjacent inventory locations to operate conductive elements ina capacitive sensor, according to some implementations.

FIG. 14 depicts a flow diagram of a process for using a conductiveelement as part of a capacitive sensor and as a radio frequency antenna,according to some implementations.

FIG. 15 depicts a flow diagram of a process for using capacitance datato select a particular configuration of an antenna matching network,according to some implementations.

FIG. 16 depicts a flow diagram of a process for using a conductiveelement as part of a capacitive sensor and a radio frequency antenna,with the tuning of that antenna being based on capacitance data,according to some implementations.

FIG. 17 depicts a flow diagram of a process for using capacitance datato determine the change in quantity of an item stowed at an inventorylocation, according to some implementations.

FIG. 18 depicts a flow diagram of a process for using capacitance datato generate event data about an inventory location, according to someimplementations.

While implementations are described herein by way of example, thoseskilled in the art will recognize that the implementations are notlimited to the examples or figures described. It should be understoodthat the figures and detailed description thereto are not intended tolimit implementations to the particular form disclosed but, on thecontrary, the intention is to cover all modifications, equivalents, andalternatives falling within the spirit and scope as defined by theappended claims. The headings used herein are for organizationalpurposes only and are not meant to be used to limit the scope of thedescription or the claims. As used throughout this application, the word“may” is used in a permissive sense (i.e., meaning having the potentialto), rather than the mandatory sense (i.e., meaning must). Similarly,the words “include,” “including,” and “includes” mean “including, butnot limited to”.

DETAILED DESCRIPTION

Described in this disclosure are systems and techniques for generatinginteraction data at an inventory location, such as in a materialshandling facility (facility). The facility may include, or have accessto, an inventory management system. The inventory management system maybe configured to maintain information about items, users, condition ofthe facility, and so forth. For example, the inventory management systemmay maintain data indicative of a number of items at a particularinventory location, what items a particular user is ordered to pick, howmany items have been picked or placed at the inventory location,requests for assistance, environmental status of the facility, and soforth.

Operation of the facility may be facilitated by using one or moresensors to acquire information about interactions in the facility. Theinventory management system may process the sensor data from the one ormore sensors to determine interaction data. The interaction data isindicative of action such as picking or placing an item at a particularinventory location, presence of the user at the inventory location, andso forth. For example, the inventory management system may use thesensor data to generate interaction data that determines a type of itema user picked from a particular inventory location.

An inventory location may include shelves, hangers, and so forth, thathold or otherwise support a type of item. The inventory location may bearranged into sections, such as lanes on a shelf. For example, a shelfmay have three lanes, with each lane holding a different type of item.Items may be added to (placed) or removed (picked) from the inventorylocation, moved from one inventory location to another, and so forth.

The inventory location may include capacitive sensors, weight sensors,image sensors, and so forth. An inventory management system may use thedata from these sensors to determine the interaction data. Thecapacitive sensors may include one or more conductive elements arrangedat or proximate to the inventory location. For example, an array ofconductive elements may be arranged beneath a protective layer of theshelf. Objects on or above the conductive elements may result in aparticular electrical capacitance of the particular conductive element.For example, the presence of an item may result in a first capacitancevalue while the absence of the item may result in a second capacitancevalue. Given a previously known arrangement of the conductive elements,an estimated location of an interaction or an object may be determined.The interaction may include an item being picked or placed to theinventory location, the hand of the user, a manipulator of robot, and soforth.

The array of conductive elements may comprise a variety of differentarrangements of the conductive elements. In one arrangement, theconductive elements may be approximately rectangular and arranged in oneor more rows and columns. A location of an object may be determinedusing the capacitive values from a plurality of the conductive elements.For example, a minimum mean square error estimation function may use thecapacitance values from two or more conductive elements to determine anestimated location of an object relative to those conductive elements.In another arrangement, the conductive elements may be arranged as righttriangles, with two triangles arranged such that their respectivelongest sides are adjacent to one another. A location of an object maybe determined with respect to these triangular conductive elements basedat least in part on a ratio or difference between the capacitance valuesobtained by each. For example, the first capacitance value obtainedusing a first triangular conductive element and a second capacitancevalue obtained using a second triangular conductive element may beapproximately the same when a homogenous object is located proximate tothe midpoints of the longest sides of each triangle.

The capacitive sensors may be configured to utilize a far-fieldcapacitance effect. The far-field capacitance effect may be determinedby measuring the self-capacitance of the conductive elements, ratherthan a mutual capacitance. In one implementation, a known charge may beprovided to the conductive element, and the resultant voltage may bemeasured between the conductive element and the ground. A shieldcomprising an electrical conductor may be arranged along one or moreside of the conductive element. For example, the shield may be separatedfrom the conductive element by an electrical insulator. Duringoperation, the shield may be driven to the same, (or a substantiallysimilar) electrical potential as that provided to the conductiveelement. As a result of this, a voltage difference that is below athreshold voltage is present between the shield and the conductiveelement. In some implementations, the voltage difference may be zero.The shield in this configuration directs the electric field generallyaway from the shield. This directionality may be used to preventerroneous readings for objects on the back side of the conductiveelement, such as may occur in an unshielded configuration. In someimplementations, a ground plane may be arranged behind the shield,opposite the conductive element. The ground plane may comprise anelectrically conductive material that is separated from the shield by anelectrical insulator. The ground plane may be connected to an earthground in some implementations.

Proximity of an object to the conductive element or contact by theobject to the conductive element affects the charge on the conductiveelement, producing a change in the resultant voltage that may then bemeasured and used to determine a capacitance value.

During operation, a switch module may comprise circuitry used to switchor selectively connect a capacitive sensor module to a particularconductive element. Once connected, a charge may be applied to theconductive element. A voltage, capacitance, resistance, or otherelectrical characteristic may be measured by the capacitive sensormodule which may then generate capacitance data.

The switch circuitry may scan across a plurality of conductive elementsin a particular sequence. During operation, the switch circuitry may bedesigned to prevent the operation of two conductive elements that areadjacent to one another to avoid interference. For example, the firstconductive element and second conductive element that are side-by-sideon a shelf would not be activated simultaneously. The conductiveelements for each shelf or group of shelves on a rack may be drivenusing a particular capacitive sensor module and a corresponding switchmodule. In a relatively dense environment, such as a warehouse whereshelves on adjacent racks may be located side-by-side, operation of oneshelf with a corresponding set of capacitive sensor modules and switchmodules may result in interference to an adjacent shelf and itscorresponding set of capacitive sensor modules and switch modules.

To mitigate or eliminate potential interference between capacitivesensors at adjacent inventory locations, operation of these capacitivesensors may be coordinated to avoid mutual interference by using a seedvalue. A particular grouping of capacitive sensors having conductiveelements under the control of the switch module, such as on a particularshelf or group of shelves, is assigned a particular seed value. Thisseed value differs from the seed value used by an adjacent grouping ofcapacitive sensors. The seed value may be used to generate sequencedata, which may then be used to control the operation of the capacitivesensors. For example, a first rack of shelves that have a first commoncapacitive sensor module and first switch module may receive a firstseed value that is used to generate first sequence data. Sequence datamay comprise a set of data elements, each element indicative of aconductive element. The sequence data may then be used to operate theconductive elements. Continuing the example, a second rack of shelvesadjacent to the first rack of shelves has a second common capacitivesensor module and a second switch module that receives a second seedvalue different from the first seed value. From this second seed value,second sequence data is generated. The first sequence data and thesecond sequence data differ from one another, resulting in differentsequences in which the conductive elements are activated between theadjacent shelves. By using different sequences, mutual interferencebetween capacitive sensors may be reduced or eliminated.

In another implementation, sequence data may be generated by using theseed value that has been received from an external device, such as aserver, as an input to a random or pseudorandom function. Bycoordinating the distribution of seed values such that physicallyadjacent devices receive different seed values, the resulting sequencedata between adjacent capacitive sensors differs and interference ismitigated. In some implementations, the sequence data may include pausessuch as a particular time delay value.

Use of the seed value thus prevents adjacent capacitive sensors fromentering into a phase lock situation that results in persistent orongoing interference. While it may still be possible in someimplementations for two adjacent capacitive sensors to be activatedcontemporaneously and produce interference with one another, thefrequency and duration of such interference is significantly reduced.

In some implementations, the conductive elements that are part of thecapacitive sensor may serve multiple purposes. For example, the switchmodule may selectively connect a particular conductive element either tothe capacitive sensor module or to a radio module. While connected tothe capacitive sensor module, the conductive element may be used todetermine a capacitance value. While connected to the radio module, theconductive element may be used as an antenna to send or receive radiofrequency signals. In one implementation, a change in the capacitancedata that exceeds a threshold value may result in the switch moduleconnecting the conductive element to the radio module. The radio modulemay then send a signal, such as an interrogation signal or a radiofrequency identification (RFID) tag, using the conductive element as anantenna. After a radio frequency response has been received, orpredetermined amount of time elapses, the switch module may disconnectthe conductive element from the radio module and reconnect conductiveelement to the capacitive sensor module. As described above, in someimplementations, sequence data may be used to control the order in whichconductive elements are operated for generation of capacitance data.

The radio module may use an antenna matching network module to connectto a dedicated antenna or to the conductive element acting as anantenna. The antenna matching network module may also be known as anantenna tuner, transmatch, antenna tuning unit, antenna coupler, and soforth. The antenna matching network module may include one or moreelectronic components such as inductors, capacitors, resistors,transformers, and so forth, that may be configured to provide aparticular impedance match between the radio module and the antenna.Data indicative of a particular matching network configuration may beused to configure the antenna matching network module to provide aparticular impedance match. For example, the matching networkconfiguration may specify a particular configuration or connectionbetween inductors and capacitors of particular values to providecircuitry that will transform one impedance to another impedance.

The impedance of an antenna and associated feedline may vary based on avariety of factors, including frequency of operation, proximity of otherobjects, and so forth. For example, an object that is close to anantenna may change the impedance of that antenna. By utilizing theantenna matching network module to perform impedance matching betweenthe radio module and the antenna, power transfer between the two may beimproved. Also, in some situations, an impedance mismatch that isgreater than the threshold value may result in power from thetransmitter of the radio module being reflected back to the finalamplifier of the radio module. Such reflected power may, in somesituations, result in damage to the transmitter of the radio module.

The capacitance data obtained from a capacitive sensor may be used asinput for the antenna matching network module to determine a particularmatching network configuration. For example, the capacitance data may beused to determine that no object is near the antenna. Based on thisinformation, a first matching network configuration may be used.Subsequently, an object such as a user, item, and so forth, is moved andplaced near the antenna. The presence of this object may change theimpedance of the antenna. The capacitance data is used to determine thatthe object is near the antenna, and subsequently, a second matchingnetwork configuration is then used. As a result, a better impedancematch between the radio module and the antenna may be obtained thatimproves overall performance, prevents damage to the radio module, andso forth. The antenna connected to the antenna matching network modulemay be one or more of the conductive element or a dedicated antenna.

An event detection module may be used to generate event data associatedwith a particular inventory location. Event data may compriseinformation that is indicative of a change associated with an inventorylocation. For example, the event data may indicate that one or moretypes of sensor data have exceeded particular thresholds or met otherconditions.

The event data may be used to trigger or initiate other actions. Forexample, event data may indicate occurrence of a change in capacitancevalues that exceeds a threshold value. Responsive to this event data,processing of weight data obtained from weight sensors at a timecorresponding to the event data may take place. Weight data alone issubject to noise, such as vibration or other movements in theenvironment. By using the event data, such as obtained from a capacitivesensor, a particular portion or set of weight data may be considered,mitigating the effects of noise. The capacitive sensor and resultingcapacitance data is relatively immune to the sources of noise thataffect the weight sensor, such as vibration. The event data may be usedto determine the weight data used to generate interaction data. Forexample, based on a change in weight from before and after the time ofthe event data, a quantity of a type of item added to or removed fromthe inventory location may be determined.

A variety of other techniques are also described to generate theinteraction data. For example, an estimated location of an interactionwith respect to the inventory location may be determined using one ormore of the capacitance data, the weight data, image data, or othersensor data. A particular type of item may be associated with theestimated location. For example, capacitance data may indicate a changein capacitance at a first lane on the shelf, weight data may indicate achange in weight that corresponds to the first lane, image data mayprocessed to determine movement within the first lane, and so forth.Continuing the example, based at least in part on this sensor data,interaction data may be generated that indicates a particular quantityof a particular type of item was picked or placed from the lane.

By using the techniques described herein, operation of the facility maybe improved. Details about interactions between users and items in thefacility may be quickly and accurately determined. For example, as itemsare picked, placed, and so forth, information such as inventory levelsbased on changes in the count of items at the inventory locations may bereadily and more accurately determined. As a result, the inventorymanagement system may be able to quickly track what item a user hasinteracted with, maintain up-to-date inventory information, and soforth.

Illustrative System

FIG. 1 illustrates a system 100 using a variety of sensors to generateinteraction data about an inventory location, according to someimplementations. A shelf 102 may include one or more lanes 104. Thelanes 104 are an area upon the shelf 102 that is associated with aparticular type of item 106. For example, the shelf 102 depicted herehas three lanes 104(1), 104(2), and 104(3). First lane 104(1) may beassociated with storing some quantity of first item 106(1) such as catfood, while second lane 104(2) may be associated with storing somequantity of second item 106(2) such as dog food, and third lane 104(3)may be associated with storing some quantity of third item 106(3) suchas fish food. Shelves 102 may be adjacent to one another. As depictedhere, the first shelf 102(1) is adjacent to the second shelf 102(2).

A user 108 may interact with the shelf 102 or the lanes 104 at the shelf102. For example, the user 108 may remove first item 106(1) from thefirst lane 104(1) and place the first item 106(1) into a tote 110. Thetote 110 may be associated with the user 108 or that user's 108 useraccount. The user 108 may pick items 106 from the shelf 102 and placethem in the tote 110, or may place items 106 from the tote 110 to theshelf 102.

Each shelf 102 may include or be associated with one or more sensors112. The sensors 112 may be positioned to gather information about theshelf 102 or other type of inventory location. Circuitry associated withthe one or more weight sensors 112(1) generates weight data 114. Forexample, a weight sensor 112(1) may be arranged proximate to each of thefour corners of a rectangular shelf 102. Output from each weight sensor112(1) may be used to produce the weight data 114 that includes weightvalues for each of the corners at particular times.

The shelf 102 may include or be proximate to one or more capacitivesensors 112(2). For example, the conductive elements of the capacitivesensor 112(2) may be located on, in, or below a surface of the shelf 102upon which items 106 may rest. Various structures and arrangements ofthe conductive elements are described in more detail with regard toFIGS. 2, 3, and elsewhere within the application.

The capacitive sensor 112(2) includes capacitive sensing circuitry thatgenerates capacitance data 116. The capacitive sensing circuitry may usevarious techniques to determine capacitance. For example, the capacitivesensing circuitry may include a source that provides a predeterminedvoltage, a timer, and circuitry to measure voltage of the conductiveelement relative to the ground. By determining an amount of time that ittakes to charge the conductive element to a particular voltage, thecapacitance may be calculated. The capacitive sensing circuitry may useone or more of analog or digital circuits to determine capacitance.During operation the capacitive sensor 112(2) that has a conductiveelement beneath a first lane 104(1) may produce capacitance data 116indicating capacitance values at particular times.

One or more image sensors 112(3) may be used to acquire image data 118at or near the shelf 102 or other inventory location. The image data 118may comprise one or more still images, video, or combination thereof.The image sensor 112(3) may have a field of view (FOV) 120 that includesat least a portion of the shelf 102 or other type of inventory location.For example, a camera may be mounted within the shelf 102 to acquireimage data 118 of one or more lanes 104.

In some implementations, each lane 104 or group of lanes 104 may haveone or more respective sensors 112. For example, each lane 104 may haveor be associated with one or more of a weight sensor 112(1), capacitivesensor 112(2), image sensor 112(3), and so forth. Each lane 104 may haveone or more of each type of sensor 112 as well.

An inventory management system 122 may access the sensor data generatedby the sensors 112. The inventory management system 122 may beconfigured, as described below, to perform various functions suchtracking changes to a quantity on hand of the items 106 at the shelf102.

The inventory management system 122 may include or have access to ananalysis module 124. The analysis module 124 may access informationincluding, but not limited to, item data 126, sensor data, or otherinformation.

The item data 126 provides information about a particular type of item106, including characteristics of that type of item 106 such as physicaldimensions, where that type of item 106 is located in the facility,characteristics about how the item 106 appears, capacitance valuesassociated with the type of item 106, and so forth. For example, theitem data 126 may indicate that the type of item 106 is “Bob's Low FatBaked Beans, 10 oz can” with a stock keeping unit number of “24076513.The item data 126 may indicate the types and quantities of items 106that are expected to be stored at that particular inventory locationsuch as in a particular lane 104 on a shelf 102, width and depth of thattype of item 106, weight of the item 106 individually or in aggregate,sample images of the type of item 106, and so forth.

The item data 126 may include an item identifier. The item identifiermay be used to distinguish one type of item 106 from another. Forexample, the item identifier may include a stock keeping unit (SKU)string, Universal Product Code (UPC) number, radio frequencyidentification (RFID) tag data, and so forth. The items 106 that are ofthe same type may be referred to by the same item identifier. Forexample, cans of beef flavor Brand X dog food may be represented by theitem identifier value of “9811901181”. In other implementations,non-fungible items 106 may each be provided with a unique itemidentifier, allowing each to be distinguished from one another.

The item data 126 may include one or more geometry data, item weightdata, sample image data, sample capacitance data, or other data. Thegeometry data may include information indicative of size and shape ofthe item 106 in one, two, or three dimensions. For example, the geometrydata may include the overall shape of an item 106, such as a cuboid,sphere, cylinder, and so forth. The geometry data may also includeinformation such as length, width, depth, and so forth, of the item 106.Dimensional information in the geometry data may be measured in pixels,centimeters, inches, arbitrary units, and so forth. The geometry datamay be for a single item 106, or a package, kit, or other groupingconsidered to be a single item 106.

The item weight data comprises information indicative of a weight of asingle item 106, or a package, kit, or other grouping considered to be asingle item 106. The item data 126 may include other data. For example,the other data may comprise weight distribution of the item 106, pointcloud data for the item 106, and so forth.

The sample capacitance data may comprise data indicative of a previouslymeasured or calculated change in capacitance a representative capacitivesensor 112(2) based on the presence or absence of a sample of the typeof item 106. For example, during processing or intake of the item 106 atthe facility, a sample of the type of item 106 may be placed on acapacitive sensor 112(2) to generate the sample capacitance data.

The sample image data may comprise one or more images of one or more ofthat type of item 106. For example, sample image data may be obtainedduring processing or intake of the item 106 to be used by the facility.

The item data 126 may include one or more inventory location identifiers(IDs). The inventory location ID is indicative of a particular area orvolume of an inventory location such as a shelf 102 that is designatedfor stowage of the type of item 106. For example, a single shelf 102 mayhave several lanes 104, each with a different inventory location ID.Each of the different inventory location IDs may be associated with alane 104 having a particular area on the shelf 102 designated forstorage of a particular type of item 106. A single type of item 106 maybe associated with a particular inventory location ID, a plurality ofinventory location IDs may be associated with the single type of item106, more than one type of item 106 may be associated with theparticular inventory location ID, and so forth.

The item data 126 may also include quantity data. The quantity data maycomprise a count or value indicative of a number of items 106. The countmay be a measured or an estimated value. The quantity data may beassociated with a particular inventory location ID, for an entirefacility, and so forth. For example, the same type of item 106 may bestored at different shelves 104 within the facility. The quantity datamay indicate the quantity on hand for each of the different inventorylocations.

The analysis module 124 may utilize the weight data 114, the capacitancedata 116, the image data 118, the item data 126, and other informationto generate interaction data 128. The interaction data 128 is indicativeof action such as picking or placing an item 106 for a particularinventory location, presence of the user 108 at the inventory location,and so forth. Operation of the analysis module is described in moredetail below with regard to FIG. 6 and elsewhere.

FIG. 2 illustrates at 200 the arrangement of conductive elements used aspart of a capacitive sensor 112(2) and weight sensors 112(1) at aninventory location, according to some implementations. A top view 202 ofa shelf 102 and side view 204 of an enlarged portion of the shelf 102are depicted.

As shown in the top view 202, a plurality of conductive elements 206 aredistributed in rows and columns across the shelf 102 to form an array.Arranged proximate to each of the four corners of the shelf 102 areweight sensors 112(1).

As shown in the side view 204, the conductive elements 206 may beconnected by wire or other electrical conductor. The wire transfers acapacitive signal 208 between the conductive element 206 and othercircuitry, such as a switch module 210. The switch module 210 may inturn connect to a capacitive sensor module 212. For example, thecapacitive signal 208 may be used to supply a charge to the conductiveelement 206. The switch module 210 may comprise switching circuitry thatallows for the capacitive sensor module 212 to be selectively connectedto a particular conductive element 206. In some implementations, aplurality of switch modules 210 may be used to allow for differentswitching configurations. For example, a first switch module 210(1) mayhave 4 outputs, each connecting to additional switch modules 210(2),210(3), 210(4), 210(5). Each of those switch modules 210(2)-(5) may have4 outputs in which each output is connected to additional switch modules210, and so forth. The switching circuitry may comprisemicroelectromechanical switches, relays, transistors, diodes, and soforth. Other configurations or networks of switch modules 210 may beimplemented as well.

The capacitive sensor module 212 may be used to generate the capacitancedata 116. The capacitance data 116 may include information such as acapacitance value, information indicative of a particular conductiveelement 206, timestamp, and so forth. In some implementations, circuitryor functionality of the switch module 210 and the capacitive sensormodule 212 may be combined.

A bottom plate 214 may provide mechanical support for one or more of theconductive elements 206. In some implementations, the bottom plate 214may comprise an electrical conductor that acts as a shield for anelectric field present at the conductive element 206.

A shelf top 216 may be arranged atop one or more of the conductiveelements 206 and the bottom plate 214. One or more items 106 may rest onor above the shelf top 216. For example, the shelf top 216 may comprisea nonconductive material such as a plastic or ceramic.

The conductive element 206 may comprise one or more electricallyconductive materials. The electrically conductive elements 206 may beformed as one or more of a coating, thin-film, paint, depositedmaterial, foil, mesh, and so forth. For example, the conductive element206 may comprise an electrically conductive paint, silver paste,aluminum film, a copper sheet, and so forth. The conductive element 206may be deposited upon, embedded within, laminated to, or otherwisesupported by the bottom plate 214, the shelf top 216, and so forth.These conductive elements 206 may then be connected to the capacitivesensing circuitry in the capacitive sensor module 212.

One or more shields 220 may be provided. A shield 220 may be adjacent toone or more of the conductive elements 206. The shield 220 comprises anelectrically conductive material and is separated by an electricalinsulator, such as air, plastic, ceramic, and so forth, from theconductive element 206. A single shield 220 may be used to provideshielding for one or more conductive elements 206. During operation, theshield 220 may be driven at the same voltage potential of the input ofthe capacitive signal 208. In this configuration, there is no differencein electrical potential between the shield 220 and the conductiveelement 206. External interference may then couple to the shield 220producing little interaction with the conductive element 206. The shield220 may also be used to direct the electric field produced by theconductive element 206 during operation. For example, the electric fieldis directed generally away from the shield 220. Using this technique,the capacitive sensor 112(2) may detect objects on the side oppositethat of the shield 220, with the shield 220 preventing the sensor from“seeing” or being affected by an object behind the shield 220.

Additional layers, omitted for clarity, may be present. For example, anelectrical insulator 222 such as polyethylene terephthalate may bearranged between the bottom plate 214 and the shield 220 (if present) orthe conductive element 206. Wires, circuit traces, or other electricallyconductive pathways may conduct the capacitive signal 208 between thecapacitive sensor module 212 and the conductive element 206.

The bottom plate 214 may be supported by one or more of the weightsensors 112(1). In some implementations, the bottom plate 214 maycomprise an electrically conductive material and act as a ground plane,such as if connected to an earth ground. The one or more of the weightsensors 112(1) may be connected to the weight sensor module 218. Theweight sensor module 218 may comprise circuitry that is used to generatethe weight data 114. The weight data 114 may include information such asa weight value, information indicative of a particular weight sensor112(1), timestamp, and so forth. In some implementations, circuitry orfunctionality of the weight sensor module 218 and the weight sensor112(1) may be combined.

FIG. 3 illustrates several layouts 300 of arrays of conductive elements206 used for capacitive sensing, according to some implementations.

A first layout 302 illustrates the conductive elements 206 arranged inrows and columns, with each of the conductive elements 206 having agenerally rectangular shape. In other implementations, each of theconductive elements 206 may have different shapes, such as circles,regular polygons, irregular polygons, and so forth.

A second layout 304 illustrates the conductive elements 206 arranged inrows and columns, with each of the conductive elements 206 beinggenerally square in shape.

A third layout 306 depicts the conductive elements 206 arranged inpairs, with each pair comprising a first triangular conductive element206 and a second triangular conductive element 206. Each of thetriangular conductive elements 206 has three sides forming thetriangular shape, as well as a top and a bottom. Each of the triangularconductive elements 206 may be a right triangle. The longest side ofeach triangle, or hypotenuse, may be arranged to be adjacent to thelongest side of the other triangle in the pair. When arranged in thismanner, an overall shape of the pair of triangular conductive elements206 is generally square or rectangular. With regard to the third layout306, a long axis through the pair of triangular conductive elements 206extends from a front to a back of the shelf 102 or other inventorylocation.

A fourth layout 308 depicts an arrangement in which the conductiveelements 206 are triangular conductive elements 206 arranged adjacent toone another. In the arrangement depicted, the triangles are isoscelestriangles. In other implementations, other types of triangles or othergeometric shapes may be used.

A fifth layout 310 depicts another arrangement in which the conductiveelements 206 are arranged in pairs, with each pair comprising a firsttriangular conductive element 206 and a second triangular conductiveelement 206. With regard to the fifth layout 310, a long axis throughthe pair of triangular conductive elements extends from a left to aright of the shelf 102 or other inventory location.

A sixth layout 312 depicts an arrangement in which the conductiveelements 206 comprise strips or rectangles having a long axis thatextends from the front to the back of the shelf 102 or other inventorylocation. In some implementations, the width of the strips may beconfigured to be less than or equal to the width of a lane 104.

A seventh layout 314 depicts an arrangement in which the conductiveelements 206 are arranged along a front edge of the shelf 102 or otherinventory location. The front edge is that portion of the shelf 102 orinventory location that is closest to the user 108 during use. With thislayout, the capacitive sensors 112(2) with their conductive elements 206arranged along the front edge may be used to detect a presence of anobject such as an item 106 or a portion of the user 108 such as theirhand at the shelf 102. Data indicative of the proximity of an object,such as event data, may be used to trigger other actions such asdesignating particular weight data 114 corresponding to a particulartime for further analysis.

In other implementations, other layouts may be used. Similarly, othergeometric shapes may be used to form the conductive elements 206. Forexample, the conductive elements 206 depicted in the second layout 304may be implemented as circles. FIG. 4 illustrates a block diagram 400 ofusing a seed value to generate a sequence of operation of conductiveelements 206 in capacitive sensors 112(2). In some situations, operationof adjacent capacitive sensors 112(2) may result in interference. Forexample, when a first capacitive sensor module 212(1) on a first shelf102(1) activates a first conductive element 206(1), a second conductiveelement 206(2) located on adjacent second shelf 102(2) may pick upinterference that is detected by a second capacitive sensor module212(2) on the second shelf 102(2), or vice versa.

It is advantageous to avoid contemporaneous operation of adjacentconductive elements 206 to mitigate this interference. In somesituations, the capacitive sensor module 212 and associated circuitrysuch as a switch module 210 may be synchronized to operate inconjunction with adjacent units. However, such synchronization mayrequire specialized timing circuitry or other configuration that may beexpensive or difficult implement.

To minimize the likelihood of operation by adjacent conductive elements206, a seed value 402 may be used. The seed value 402 may comprise aninitial value or other information. For example, the seed value 402 maycomprise a random number, media access control (MAC) address value of acommunication interface associated with the capacitive sensor module212, and so forth.

The seed value 402 may be provided by a dedicated server, controller, orother device external. For example, a server may send the seed value 402using a network. Issuance of the seed value 402 to a particular deviceat a particular inventory location may be made by taking intoconsideration the physical layout of the facility. For example, a set ofpredefined seed values 402 may be reused within the same facility solong as the same seed value 402 is not issued to two devices that haveconductive elements 206 within a threshold distance of one another.

A sequencer module 404 may accept as input the seed value 402. In someimplementations, the sequencer module 404 may also accept as input atime delay value 406. The time delay value 406 may specify one or moreof a duration of time, value indicative of a probability, valueindicative of weighting, and so forth. For example, the time delay value406 may specify a duration of 5 milliseconds (ms) and weighting value of“0.001”. The weighting value may be used to indicate how often the timedelay value 406 is inserted into the sequence.

The sequencer module 404 is configured to generate sequence data 408.The sequence data 408 may comprise data elements, each data elementbeing representative of a particular capacitive sensor 112(2). Thesequence data 408 indicates a particular order in which the capacitivesensors 112(2) or the conductive elements 206 thereof are to beoperated. For example, as depicted in FIG. 2, each shelf 102 may includea single capacitive sensor module 212 that may be selectively connectedto the 10 conductive elements 206 on that shelf 102 using the switchmodule 210. The sequencer module 404 may utilize a predefined hashfunction, random number generator, pseudorandom number generator, orother function that when provided with a seed value 402 generates aparticular set of sequence data 408. In some implementations, thefunction used by the sequencer module 404 may be deterministic, such asa hash that always produces the same sequence data 408 given aparticular seed value 402. In other implementations, the function usedby the sequencer module 404 may be nondeterministic orpseudo-nondeterministic, such as a pseudorandom number generator thatproduces an output value that is used in conjunction with the seed value402 to generate the sequence data 408.

The time delay value 406 may be used as an input to the sequencer module404. By implementing the use of the time delay value 406, an intentionaljitter may be introduced in the timing of the sequence data 408,changing when the particular conductive elements 206 are operated. Forexample, the time delay value 406 as described above may indicate a 5 msdelay. An instruction or control character indicative of this 5 ms delaymay be randomly or pseudo-randomly injected into the sequence data 408.During operation, the switch module 210 may utilize the sequence data408 to determine a sequence of operation, such as the order in whichparticular conductive elements 206 are to be energized and have theirassociated capacitance values determined. The switch module 210 mayprocess the instruction or control character indicative of the delay bypausing or temporarily extending or shortening the interval of operationof a particular conductive element 206, for all conductive elements 206,and so forth. In one example, the traversal of the sequence specified bythe sequence data 408 may be suspended for duration of the timespecified by the time delay value 406. In another example, a dwell timeor time spent sampling using a particular conductive element 206 may beincreased. In another example, a dwell time or sample time for one ormore of the first plurality of capacitive sensors 112(2) may beincreased by a duration of time specified by the time delay value 406.For example, during typical operation the capacitive sensor module 212may obtain samples at a rate of 100 samples per second. In this example,each conductive element 206 is being used by the capacitive sensormodule 212 for 10 ms. When the time delay value 406 is applied, the timespent using one or more of the conductive elements 206 may be extendedto 15 ms.

As a result, the overall timing as to when a particular conductiveelement 206 that appears later with the sequence data 408 will beactivated is changed. For example, one conductive element 206 may bescanned for an allocated amount of time plus that specified by the timedelay value 406. Use of the time delay value 406 may mitigate oreliminate situations in which two adjacent devices are activatedcontemporaneously with one another.

The sequence data 408 may describe a sequence of operation of triangularconductive elements 206 that does not correspond with the physicalarrangement of those conductive elements 206. For example, referring tothe arrangement of conductive elements 206 described in FIG. 2, thesequence data 408 may be configured to avoid sequential operation ofconductive elements 206 that may be adjacent to another device.Continuing the example, the sequence data 408 may be configured to avoidoperation of conductive element 206(1) followed by conductive element206(6) and operation of conductive element 206(5) followed by conductiveelement 206(10) to minimize the duration of interference with adjacentdevices.

As the switch module 210 cycles through the conductive elements 206 inthe order specified by the sequence data 408, the capacitive sensormodule 212 generates capacitance data 116 as described above.

FIG. 5 illustrates a block diagram 500 of conductive elements 206connected via a switch module 210 to a capacitive sensor module 212 orantenna matching network, according to some implementations. In otherimplementations, the various modules described herein may be connectedin different ways.

As described above, the capacitive sensor module 212 generatescapacitance data 116. The capacitive sensor module 212 may be configuredin some implementations to generate proximity data 502. As an objectapproaches the capacitive sensor 112(2), the capacitance that itmeasures may change, providing an indication of the presence of theobject. For example, a capacitance change value may be determined bycalculating a difference between a first capacitance value obtained atfirst time and a second capacitance value obtained a second time.Language indicative of a particular time may indicate a particularinstant in time or a particular interval of time that begins or ends atthe designated particular time.

If the capacitance change value exceeds a threshold value, proximitydata 502 may be generated. In some implementations, the analysis module124 may generate the proximity data 502, or may generate event data thatis indicative of proximity of an object. The object may comprise a user108, or portion thereof such as a hand, and item 106, a robot, and soforth.

As described above, the capacitive sensor module 212 may send or receivea capacitive signal 208 to a particular conductive element 206. Thecapacitive signal 208 may comprise either a direct current transfercharge, or an alternating current transfer charge. In someimplementations, the switch module 210 may be used to provide electricalpathway between the capacitive sensor module 212 and one or more of theconductive elements 206. For example, the switch module 210 may comprisean array of field effect transistors (FETs) that may be configured toprovide an electrical pathway between particular inputs and particularoutputs of the switch module 210.

During operation, the switch module 210 may use switch control data 504to determine which of the inputs to the switch module 210 are connectedto particular ones of the outputs on the switch module 210. The switchcontrol data 504 may include one or more of the sequence data 408, eventdata 506, the proximity data 502, or other information. For example,switch control module 210 may use the sequence data 408 to operateindividual ones the conductive elements 206 and the order specified bythe sequence data 408. This process of scanning through the conductiveelements 206 in the order specified by the sequence data 408 andgenerating the corresponding capacitance data 116 may occur untilinformation such as proximity data 502 or event data 506 is received.For example, receipt of the proximity data 502 or event data 506indicating a change at the shelf 102 may result in the switch module 210suspending the use of the specified sequence data 408 to provide fordifferent interconnection, such as described below with regard to theradio module 508.

In some implementations, the device at the inventory location mayinclude a radio module 508. The radio module 508 may comprise one ormore of a transmitter or a receiver that operates to generate or detectradio frequency signals 510. For example, the radio module 508 maycomprise an RFID compatible system that is able to generate an RFIDinterrogation signal and receive a response from an RFID tag. The radiofrequency signals 510 may include those signals having a frequency of atleast 50 kHz.

The radio module 508 may be connected to or may otherwise incorporate anantenna matching network module 512. The antenna matching network module512 may also be known as an antenna tuner, transmatch, antenna tuningunit, antenna coupler, and so forth. The antenna matching network module512 may include one or more electronic components such as inductors,capacitors, resistors, transformers, and so forth, that may beconfigured to provide a particular impedance match between the radiomodule 508 and an antenna. Data indicative of a particular matchingnetwork configuration 514 may be used to configure the antenna matchingnetwork module 512 to provide a particular impedance match. For example,the matching network configuration 514 may specify a particularconfiguration or connection between inductors and capacitors ofparticular values to provide circuitry that will transform one impedanceto another.

The impedance of an antenna and associated feedline may vary based on avariety of factors, including frequency of operation, proximity of otherobjects, and so forth. For example, an object that is close to anantenna may change the impedance of that antenna. By utilizing theantenna matching network module 512 to perform impedance matchingbetween the radio module 508 and the antenna, power transfer between thetwo may be improved. Also, in some situations, an impedance mismatchthat is greater than the threshold value may result in power from thetransmitter of the radio module 508 being reflected back to the finalamplifier of the radio. Such reflected power may in some situationsresult in damage to the transmitter.

The capacitance data 116 obtained from a capacitive sensor 112(2) may beused as input for the antenna matching network module 512 to determine aparticular matching network configuration. For example, the capacitancedata 116 may be used to determine that no object is near the antenna.Based on this information, a first matching network configuration 514(1)may be used. Subsequently, an object such as a user, item, and so forth,is moved and placed near the antenna. The presence of this object maychange the impedance of the antenna. The capacitance data 116 is used todetermine that the object is near the antenna, and subsequently a secondmatching network configuration 514(2) is then used. As a result, abetter impedance match between the radio module 508 and the antenna maybe obtained that improves overall performance, prevents damage to theradio, and so forth. The antenna connected to the antenna matchingnetwork module 512 may be one or more of the conductive element 206 or adedicated antenna 516.

In situations where the dedicated antenna 516 is used, the antennamatching network 512 may transfer the radio frequency signal 510 to theantenna 516. For example, a feedline or waveguide may be used totransfer the radio frequency signal 510 from the radio module 508 to theantenna 516.

In some implementations, one or more the conductive elements 206 may beused not only as parts of a capacitor to generate capacitance data 116,but they may also be used as an antenna. For example, as depicted inthis figure, output from the antenna matching network module 512 mayconnect to an input of the switch module 210. The switch module 210 maybe able to selectively connect a particular conductive element 206 tothe antenna matching network module 512. In implementations where noantenna matching network module 512 is used, output from the radiomodule 508 may be connected to the input of the switch module 210.

In the implementation described above, the switch module 210 may connectthe capacitive sensor module 212 at a first time to the conductiveelement 206. The capacitive signal 208 may be used to generatecapacitance data 116. At a second time, the switch module 210 maydisconnect the conductive element 206 from the capacitive sensor module212 and instead may connect the output of the antenna matching networkmodule 512 to the conductive element 206. In this configuration, theconductive element 206 may then act as an antenna.

Returning to the switch control module 210, as described above, theswitch control data 504 may be used to determine which of the particularinputs of the switch module 210 are connected to which of the particularoutputs of the switch module 210. For example, responsive to the receiptthe proximity data 502, the switch module 210 may connect the antennamatching network module 512 to the particular conductive element 206associated with the proximity data 502 for use as an antenna. In anotherexample, responsive to receipt of event data 506 indicating a weightchange on the shelf 102, the switch module 210 may connect the antennamatching network module 512 to the particular conductive element 206associated with the weight change for use as an antenna.

In some implementations, the switch module 210 may allow for thesimultaneous connection of both the capacitive sensor module 212 and theradio module 508 or the antenna matching network module 512 to aparticular conductive element 206. For example, the switch module 210,or other modules, may incorporate filtering or isolation circuitry toprevent the capacitive signal 208 from interfering with the radiofrequency signal 510, and vice versa.

FIG. 6 illustrates a block diagram 600 of an analysis module 124,according to some implementations. The analysis module 124 may includean event detection module 602. The event detection model 602 may acceptas input one or more of item data 126, weight data 114, capacitance data116, image data 118, or other sensor data. The event detection module602 is configured to generate event data 506. The event data 506 maycomprise information indicative of a change deemed to be significantthat is associated with an inventory location or portion thereof. Forexample, the event data 506 may be indicative of a determination by acapacitive sensor 112(2) of proximity of an object, such as an item 106or user 108. In another example, the event data 506 may comprise anindication that a weight change has exceeded a threshold value. In yetanother example, the event data 506 may indicate that motion between aplurality of images has been detected that exceeds a threshold value.The event detection module 602 may utilize one or more filter functions,comparison functions, and so forth, to determine the event data 506. Forexample, the event data 506 may result from a determination that thecapacitance data 116 and weight data 114 have each experienced changesthat exceed a threshold value. The event detection module 602 mayutilize various rules or conditions to determine the occurrence of anevent and subsequent generation of event data 506.

The analysis module 124 may include or access the data processing module604. The data processing module 604 may be configured to perform one ormore data processing functions. The data processing functions mayinclude, but are not limited to, filtering, noise reduction, signalrecovery, determination of a moving average, statistical analysis, andso forth. The data processing module 604 may accept as input sensor dataincluding, but not limited to, the weight data 114, the capacitance data116, the image data 118, and so forth. The data processing module 604may also accept as input event data 506. In one implementation, the dataprocessing module 604 may utilize the event data 506 to apply particulardata processing functions to specific data or portions thereof. Forexample, the event data 506 may indicate presence of an object based ona variation in the capacitance data 116. The event data 506 may beassociated with a particular time. The data processing module 604 mayaccess a set of the weight data 114 that was obtained before and afterthe particular time. The data processing module 604 may apply one ormore noise filters or other data processing functions to determine abaseline weight corresponding to the weight before the particular timeof the event data 506 and an event weight corresponding to the weightoccurring after the particular time.

In some implementations, the data processing module 604 may beconfigured to determine or access previously determined noise profiledata 606. For example, a particular sensor may produce characteristicnoise even when no gross changes are taking place. For example, theweight sensor 112(1) may be prone to generating noise as a result ofvibrations in the ambient environment resulting from passing traffic,users 108 moving nearby, jostling of the inventory location, and soforth. The data processing module 604 may use the event data 506 todetermine when an intentional interaction with the inventory locationhas taken place, or determine when no intentional interaction with theinventory location appears to be taking place. For example, sensor dataacquired during a period of time prior to the timestamp indicated by theevent data 506 may be accessed. The sensor data may be processed togenerate noise profile data 606. The noise profile data 606 may begenerated by processing weight values obtained from the weight sensor112(1) using one or more functions. For example, statistical functionssuch as an average, moving average, regression analysis, curve fitting,and so forth, may be used to generate the noise profile data 606. Otherfunctions such as a spectral analysis, frequency domain analysis, timedomain analysis, and so forth, may be used to generate the noise profiledata 606. The noise profile data 606 may thus provide informationindicative of the noise that is typically expected from the sensor. Oncegenerated, subsequent data associated with the event indicated by theevent data 506 may be processed to remove noise consistent with thatdescribed in the noise profile data 606. Continuing the example, thenoise profile data 606 may be subtracted from the data that occursduring the period associated with the event to produce output. In someimplementations, the noise profile data 606 may be updated based onweight data 114 that continues to be received, with the noise profiledata 606 being defined as weight values obtained before the timeassociated with the event data 506.

The data processing module 604 may be configured to generate processeddata, such as processed weight data 608, processed capacitance data 610,and so forth. The processed weight data 608 may comprise a baselineweight corresponding to the weight occurring before the particular timeof the event data 506. The processed weight data 608 may also include anevent weight corresponding to the weight occurring after the particulartime. The processed capacitance data 610 may comprise a firstcapacitance value corresponding to a capacitance occurring before theparticular time of an event 506 and a second capacitance valuecorresponding to a capacitance occurring after the particular time. Insome implementations, the processed capacitance data 610 may result fromthe application of one or more statistical techniques, noise reduction,or signal recovery functions to the capacitance data 116. In someimplementations, the processed capacitance data 610 may comprise abaseline capacitance value that is calculated from capacitance valuesgathered during times when no events were detected. For example, basedon the sensor data such as the weight data 114 and the image data 118being indicative of no active events taking place at the inventorylocation from a first time to a second time, the capacitance data 116obtained during that interval may be used to calculate a baselinecapacitance value. The baseline capacitance value may then be used togenerate threshold data, by the interaction data module 616 foranalysis, and so forth.

The data processing module 604 may also accept as input other sensordata 612, such as input from a light sensor, accelerometer, RF receiver,and so forth. The data processing module 604 may process the othersensor data 612 to provide other processed sensor data 614. For example,the image data 118 may be processed to generate information indicativeof changes between images, object recognition data, and so forth.

Processing of one or more of the image data 118 or portions thereof maybe performed by implementing, at least in part, one or more of thefollowing tools or techniques. In one implementation, processing of theimage data 118 may be performed, at least in part, using one or moretools available in the OpenCV library as developed by Intel Corporationof Santa Clara, Calif., USA; Willow Garage of Menlo Park, Calif., USA;and Itseez of Nizhny Novgorod, Russia, with information available atwww.opencv.org. In another implementation, functions available in theOKAO machine vision library as promulgated by Omron Corporation ofKyoto, Japan, may be used to process the image data 118. In stillanother implementation, functions such as those in the Machine VisionToolbox for Matlab (MVTB) available using MATLAB as developed byMathWorks, Inc. of Natick, Mass., USA, may be utilized.

An interaction data module 616 may be configured to use as input one ormore of the item data 126, sensor data, processed data, and so forth, togenerate the interaction data 128. The interaction data module 616 mayinclude a location estimation module 618. The location estimation module618 may be configured to use one or more of the processed weight data608, the processed capacitance data 610, or the other processed sensordata 614 to determine location data 620 associated with an interaction.In some implementations, the location estimation module 618 may useunprocessed data, such as the weight data 114, capacitance data 116,image data 118, and so forth.

The location estimation module 618 may also use the item data 126 todetermine the location data 620. For example, the item data 126associated with a particular type of item 106 may be retrieved,providing information such as a width and depth, measured capacitance ofa representative sample of the type of item 106, and so forth. Thelocation estimation module 618 may use information such as the width anddepth to determine an area that item 106 is expected to cover. Based onthis information, the location information module 618 may avoidgenerating location data 620 that places the item 106 in the middle of adivider or some other unlikely location. For example, an item areaassociated with the item 106 may be determined by multiplying the widthand depth indicated by the item data 126. An inventory area designatedfor stowage of items 106 is determined that is associated with theinventory location 712. For example, lane 104(1) may be indicated in thephysical layout data as having a particular location in the facility 702and a particular width and depth. The location estimation module 618 maydetermine location data 620 indicative of a center of the item area forthe type of item 106 such that the item area is arranged within theinventory area. Continuing the example, the location estimation module618 may be configured to avoid generating location data 620 that wouldplace an item halfway off the shelf 102, or spanning a divider betweenlanes 104.

The location data 620 may provide information indicative of a particularlocation with regard to an inventory location. For example, the locationdata 620 may indicate a particular lane 104, portion of a lane 104,coordinates of the point or area on the shelf 102, and so forth. Theinteraction may comprise movement, presence, pick, place, and so forth,of an object or an item 106.

In one implementation, the location estimation module 618 may use theprocessed capacitance data 610 obtained from the array of capacitivesensors 112(2) on the shelf 102 to determine that there was a changemade to the items 106 at a given lane 104. For example, the estimatedlocation may be coarse, such as an area corresponding to a singleconductive element 206 indicated by the capacitance data 116. In anotherexample, the capacitance data 116 from a plurality of conductiveelements 206 in a known configuration, such as depicted in layouts302-312, may be used to generate the location data 620. The interactiondata module 616 may access the item data 126 to determine the type ofitem 106 stored at the lane 104(1), and other characteristics about thattype of item 106 such as a per item weight.

The interaction data module 616 may use other modules (not shown) todetermine a quantity. For example, the processed weight data 608 may beused to determine a weight change value, and this weight change valuemay be compared with the per item weight to determine a quantity ofitems 106 that have changed. A sign of the weight change may be used toindicate addition or removal of an item 106. For example, a positiveweight change value may indicate an increase in weight indicative ofplacement of an item 106 at an inventory location, while a negativeweight change value may indicate a decrease in the weight indicative ofpick of an item 106 from the inventory location. The interaction datamodule 616 with may thus generate interaction data 128. For example, theinteraction data 128 may indicate a quantity and type of item 106 thatwas removed from a particular inventory location.

In some implementations, several different types of location data 620may be generated that correspond to the same event. For example,location data 620 may be determined based on the processed weight data608, from the image data 118, and so forth. These different types oflocation data 620 may be compared, merged, or otherwise processed todetermine an overall estimated location corresponding to theinteraction.

Returning to the location estimation module 618, a variety of techniquesmay be used to determine an estimated location of an object that hasbeen introduced to or removed from the volume in which the capacitivesensor 112(2) is able to detect. As the number of capacitive sensors112(2), or the corresponding conductive elements 206 that are measuredto determine capacitance, in a given area increase, the ability to moreprecisely resolve a location is improved. For example, an object such asa hand of the user 108 that is hovering above the shelf 102 may bedetected by several different capacitive sensors 112(2), each reportinga respective capacitance value that is expressed in the capacitance data116. By analyzing the capacitance data 116, location estimation module618 may generate location data 620 that is indicative of the location ofthe hand of the user 108.

In some situations, more than one user 108 may pick or place items 106contemporaneously. For example, a first user 108(1) may remove firstitem 106(1) from first lane 104(1) while a second user 108(2) removessecond item 106(2) from second lane 104(2) at about the same time. Firstestimated location data 620(1) may be determined for the first user108(1) and second estimated location data 620(2) may be determined forthe second user 108(2). As described above, the estimated location data620 may be based at least in part on the capacitance data 116. Theweight data 114 may be processed to determine a change in center ofweight (or center of mass). By using the first estimated location data620(1) and the second estimated location data 620(2), weight changesassociated with each of the interactions may be may be determined. Forexample, given the calculated weight of items 106 stowed at the firstlane 104(1) and the second lane 104(2) and the known dimensions forthese locations, various hypotheses involving quantities of items 106 aspotentially taken by the first user 108(1) and the second user 108(2)may be compared. A hypothesis that is deemed to most closely match theweight data 114 obtained may then be used to specify the correspondingweight change and quantities associated with each interaction.

In another implementation, the location data 620 may be used toapportion the weight change associated with a change in center ofweight. Continuing the example above, the center of weight may be splitinto a plurality of possible weight changes based on the estimatedlocation data 620. If the total change in weight is 700 grams, and thecenter of weight is located at the intersection of the first lane 104(1)and the second lane 104(2), 350 grams may be designated as associatedwith the first user 108(1) and 350 grams may be designated as associatedwith the second user 108(2).

During operation, the interaction data module 616 or other modulesassociated therewith such as location estimation module 618 may generatecalibration data 622. The calibration data 622 may comprise informationthat indicates a divergence between an estimated or determined value andanother value that was either determined by different sensor, bydifferent data processing mechanism, human input, and so forth. Forexample, location estimation module 618 may generate first location data620(1) using the processed capacitance data 610. An image processingmodule (not shown) may use the image data 118 to determine a secondlocation data 620(2) using the image data 118. The second location data620(2) may be determined to be more accurate or definitive of the actuallocation associated with the interaction, and calibration data 622 maybe generated that is based on the difference between the first locationdata 620(1) and the second location at 620(2). As a result, subsequentoperation of a location estimation module 618 may utilize thecalibration data 622 during the subsequent determination of locationdata 620.

In one implementation, a ratio or comparison between the capacitancevalues obtained using two adjacent conductive elements 206 may beindicative of a relative location of an object with respect to thoseconductive elements 206. For example if the first conductive element206(1) is on the left and the second conductive element 206(2) is on theright, if a capacitance change value of the left is 27 microfarads andthe capacitance change value of the right is 2 microfarads, the locationdata 620 may indicate that the object is closer to the first conductiveelement 206(1). In another implementation, the capacitance values or aratio based on the capacitance values may be used to lookup a relativelocation associated with those values.

As the number of capacitive sensors 112(2) are increased, othertechniques may be used to process the capacitance data 116 to determinea location. For example, the data may be processed using a minimum meansquare error (MMSE) estimator, a Kalman filter or linear quadraticestimation, and so forth.

FIG. 7 is a block diagram 700 illustrating a materials handling facility(facility) 702 using the system 100, according to some implementations.A facility 702 comprises one or more physical structures or areas withinwhich one or more items 106(1), 106(2), . . . , 106(Q) may be held. Asused in this disclosure, letters in parenthesis such as “(Q)” indicatean integer value greater than or equal to zero. The items 106 maycomprise physical goods, such as books, pharmaceuticals, repair parts,electronic gear, and so forth.

The facility 702 may include one or more areas designated for differentfunctions with regard to inventory handling. In this illustration, thefacility 702 includes a receiving area 704, a storage area 706, and atransition area 708.

The receiving area 704 may be configured to accept items 106, such asfrom suppliers, for intake into the facility 702. For example, thereceiving area 704 may include a loading dock at which trucks or otherfreight conveyances unload the items 106. In some implementations, theitems 106 may be processed, such as at the receiving area 704, togenerate at least a portion of the item data 126. For example, an item106 may be imaged or otherwise scanned to develop reference images orrepresentations of the item 106 at the receiving area 704.

The storage area 706 is configured to store the items 106. The storagearea 706 may be arranged in various physical configurations. In oneimplementation, the storage area 706 may include one or more aisles 710.The aisle 710 may be configured with, or defined by, inventory locations712 on one or both sides of the aisle 710. The inventory locations 712may include one or more of a shelf 102, a rack, a case, a cabinet, abin, a floor location, or other suitable storage mechanisms for holding,supporting, or storing the items 106. For example, the inventorylocations 712 may comprise shelves 102 with lanes 104 designatedtherein. The inventory locations 712 may be affixed to the floor oranother portion of the structure of the facility 702. The inventorylocations 712 may also be movable such that the arrangements of aisles710 may be reconfigurable. In some implementations, the inventorylocations 712 may be configured to move independently of an outsideoperator. For example, the inventory locations 712 may comprise a rackwith a power source and a motor, operable by a computing device to allowthe rack to move from one location within the facility 702 to another.

One or more users 108(1), 108(2), . . . , 108(U) and totes 110(1),110(2), . . . , 110(T) or other material handling apparatus may movewithin the facility 702. For example, the user 108 may move about withinthe facility 702 to pick or place the items 106 in various inventorylocations 712, placing them on the tote 110 for ease of transport. Thetote 110 is configured to carry or otherwise transport one or more items106. For example, the tote 110 may include a basket, cart, bag, bin, andso forth. In other implementations, other material handling apparatusessuch as robots, forklifts, cranes, aerial drones, and so forth, may moveabout the facility 702 picking, placing, or otherwise moving the items106. For example, a robot may pick an item 106 from a first inventorylocation 712(1) and move the item 106 to a second inventory location712(2).

One or more sensors 112 may be configured to acquire information in thefacility 702. The sensors 112 may include, but are not limited to,weight sensors 112(1), capacitive sensors 112(2), image sensors 112(3),depth sensors 112(4), and so forth. The sensors 112 may be stationary ormobile, relative to the facility 702. For example, the inventorylocations 712 may contain weight sensors 112(1) to acquire weight sensordata of items 106 stowed therein, image sensors 112(1) to acquire imagesof picking or placement of items 106 on shelves 102, optical sensorarrays 112(14) to detect shadows of the user's 108 hands at theinventory locations 712, and so forth. In another example, the facility702 may include image sensors 112(3) to obtain images of the user 108 orother objects in the facility 702. The sensors 112 are discussed in moredetail below with regard to FIG. 8.

While the storage area 706 is depicted as having one or more aisles 710,inventory locations 712 storing the items 106, sensors 112, and soforth, it is understood that the receiving area 704, the transition area708, or other areas of the facility 702 may be similarly equipped.Furthermore, the arrangement of the various areas within the facility702 is depicted functionally rather than schematically. For example, insome implementations, multiple different receiving areas 704, storageareas 706, and transition areas 708 may be interspersed rather thansegregated in the facility 702.

The facility 702 may include, or be coupled to, the inventory managementsystem 122. The inventory management system 122 is configured tointeract with one or more of the users 108 or devices such as sensors112, robots, material handling equipment, computing devices, and soforth, in one or more of the receiving area 704, the storage area 706,or the transition area 708.

During operation of the facility 702, the sensors 112 may be configuredto provide sensor data, or information based on the sensor data, to theinventory management system 122. The sensor data may include the weightdata 114, the capacitance data 116, the image data 118, and so forth.The sensors 112 are described in more detail below with regard to FIG.8.

The inventory management system 122 or other systems may use the sensordata to track the location of objects within the facility 702, movementof the objects, or provide other functionality. Objects may include, butare not limited to, items 106, users 108, totes 110, and so forth. Forexample, a series of images acquired by the image sensor 112(3) mayindicate removal by the user 108 of an item 106 from a particularlocation on the inventory location 712 and placement of the item 106 onor at least partially within the tote 110.

The facility 702 may be configured to receive different kinds of items106 from various suppliers and to store them until a customer orders orretrieves one or more of the items 106. A general flow of items 106through the facility 702 is indicated by the arrows of FIG. 7.Specifically, as illustrated in this example, items 106 may be receivedfrom one or more suppliers, such as manufacturers, distributors,wholesalers, and so forth, at the receiving area 704. In variousimplementations, the items 106 may include merchandise, commodities,perishables, or any suitable type of item 106, depending on the natureof the enterprise that operates the facility 702.

Upon being received from a supplier at the receiving area 704, the items106 may be prepared for storage in the storage area 706. For example, insome implementations, items 106 may be unpacked or otherwise rearranged.The inventory management system 122 may include one or more softwareapplications executing on a computer system to provide inventorymanagement functions. These inventory management functions may includemaintaining information indicative of the type, quantity, condition,cost, location, weight, or any other suitable parameters with respect tothe items 106. The items 106 may be stocked, managed, or dispensed interms of countable units, individual units, or multiple units, such aspackages, cartons, crates, pallets, or other suitable aggregations.Alternatively, some items 106, such as bulk products, commodities, andso forth, may be stored in continuous or arbitrarily divisible amountsthat may not be inherently organized into countable units. Such items106 may be managed in terms of a measurable quantity such as units oflength, area, volume, weight, time, duration, or other dimensionalproperties characterized by units of measurement. Generally speaking, aquantity of an item 106 may refer to either a countable number ofindividual or aggregate units of an item 106 or a measurable amount ofan item 106, as appropriate.

After arriving through the receiving area 704, items 106 may be storedwithin the storage area 706. In some implementations, like items 106 maybe stored or displayed together in the inventory locations 712 such asin bins, on shelves 104, hanging from pegboards, and so forth. In thisimplementation, all items 106 of a given kind are stored in oneinventory location 712. In other implementations, like items 106 may bestored in different inventory locations 712. For example, to optimizeretrieval of certain items 106 having frequent turnover within a largephysical facility 702, those items 106 may be stored in severaldifferent inventory locations 712 to reduce congestion that might occurat a single inventory location 712.

When a customer order specifying one or more items 106 is received, oras a user 108 progresses through the facility 702, the correspondingitems 106 may be selected or “picked” from the inventory locations 712containing those items 106. In various implementations, item picking mayrange from manual to completely automated picking. For example, in oneimplementation, a user 108 may have a list of items 106 they desire andmay progress through the facility 702 picking items 106 from inventorylocations 712 within the storage area 706 and placing those items 106into a tote 110. In other implementations, employees of the facility 702may pick items 106 using written or electronic pick lists derived fromcustomer orders. These picked items 106 may be placed into the tote 110as the employee progresses through the facility 702.

After items 106 have been picked, the items 106 may be processed at atransition area 708. The transition area 708 may be any designated areawithin the facility 702 where items 106 are transitioned from onelocation to another or from one entity to another. For example, thetransition area 708 may be a packing station within the facility 702.When the item 106 arrives at the transition area 708, the items 106 maybe transitioned from the storage area 706 to the packing station.Information about the transition may be maintained by the inventorymanagement system 122.

In another example, if the items 106 are departing the facility 702, alist of the items 106 may be obtained and used by the inventorymanagement system 122 to transition responsibility for, or custody of,the items 106 from the facility 702 to another entity. For example, acarrier may accept the items 106 for transport with that carrieraccepting responsibility for the items 106 indicated in the list. Inanother example, a user 108 may purchase or rent the items 106 andremove the items 106 from the facility 702. During use of the facility702, the user 108 may move about the facility 702 to perform varioustasks, such as picking or placing the items 106 in the inventorylocations 712.

The interaction data 128 may provide information about an interaction,such as a pick of an item 106 from the inventory location 712, a placeof an item 106 to the inventory location 712, a touch made to an item106 at the inventory location 712, a gesture associated with an item 106at the inventory location 712, and so forth. The interaction data 128may include one or more of the type of interaction, interaction locationidentifier indicative of where from the inventory location 712 theinteraction took place, item identifier, quantity change to the item106, user identifier, and so forth. The interaction data 128 may then beused to further update the item data 126. For example, the quantity ofitems 106 on hand at a particular lane 104 on the shelf 102 may bechanged based on an interaction that picks or places one or more items106.

The inventory management system 122 may combine or otherwise utilizedata from different sensors 112 of different types. For example, weightdata 114 obtained from weight sensors 112(1) at the inventory location712 may be used instead of, or in conjunction with, one or more of thecapacitance data 116 to determine the interaction data 128.

FIG. 8 is a block diagram 800 illustrating additional details of thefacility 702, according to some implementations. The facility 702 may beconnected to one or more networks 802, which in turn connect to one ormore servers 804. The network 802 may include private networks such asan institutional or personal intranet, public networks such as theInternet, or a combination thereof. The network 802 may utilize wiredtechnologies (e.g., wires, fiber optic cables, and so forth), wirelesstechnologies (e.g., radio frequency, infrared, acoustic, optical, and soforth), or other connection technologies. The network 802 isrepresentative of any type of communication network, including one ormore of data networks or voice networks. The network 802 may beimplemented using wired infrastructure (e.g., copper cable, fiber opticcable, and so forth), a wireless infrastructure (e.g., cellular,microwave, satellite, and so forth), or other connection technologies.

The servers 804 may be configured to execute one or more modules orsoftware applications associated with the inventory management system122 or other systems. While the servers 804 are illustrated as being ina location outside of the facility 702, in other implementations, atleast a portion of the servers 804 may be located at the facility 702.The servers 804 are discussed in more detail below with regard to FIG.9.

The users 108, the totes 110, or other objects in the facility 702 maybe equipped with one or more tags 806. The tags 806 may be configured toemit a signal 808. In one implementation, the tag 806 may be a RFID tag806 configured to emit a RF signal 808 upon activation by an externalsignal. For example, the external signal may comprise a radio frequencysignal or a magnetic field configured to energize or activate the RFIDtag 806. In another implementation, the tag 806 may comprise atransmitter and a power source configured to power the transmitter. Forexample, the tag 806 may comprise a Bluetooth® Low Energy (BLE)transmitter and battery. In other implementations, the tag 806 may useother techniques to indicate presence of the tag 806. For example, anacoustic tag 806 may be configured to generate an ultrasonic signal 808,which is detected by corresponding acoustic receivers. In yet anotherimplementation, the tag 806 may be configured to emit an optical signal808.

The inventory management system 122 may be configured to use the tags806 for one or more of identification of the object, determining alocation of the object, and so forth. For example, the users 108 maywear tags 806, the totes 110 may have tags 806 affixed, and so forth,which may be read and, based at least in part on signal strength, usedto determine identity and location.

Generally, the inventory management system 122 or other systemsassociated with the facility 702 may include any number and combinationof input components, output components, and servers 804.

The one or more sensors 112 may be arranged at one or more locationswithin the facility 702. For example, the sensors 112 may be mounted onor within a floor, wall, at a ceiling, at an inventory location 712, ona tote 110, may be carried or worn by a user 108, and so forth.

The sensors 112 may include one or more weight sensors 112(1) that areconfigured to measure the weight of a load, such as the item 106, thetote 110, or other objects. The weight sensors 112(1) may be configuredto measure the weight of the load at one or more of the inventorylocations 712, the tote 110, on the floor of the facility 702, and soforth. For example, the shelf 102 may include a plurality of lanes 104or platforms, with one or more weight sensors 112(1) beneath each one toprovide weight sensor data about an individual lane 104 or platform. Theweight sensors 112(1) may include one or more sensing mechanisms todetermine the weight of a load. These sensing mechanisms may includepiezoresistive devices, piezoelectric devices, capacitive devices,electromagnetic devices, optical devices, potentiometric devices,microelectromechanical devices, and so forth. The sensing mechanisms ofweight sensors 112(1) may operate as transducers that generate one ormore signals based on an applied force, such as that of the load due togravity. For example, the weight sensor 112(1) may comprise a load cellhaving a strain gauge and a structural member that deforms slightly whenweight is applied. By measuring a change in the electricalcharacteristic of the strain gauge, such as capacitance or resistance,the weight may be determined. In another example, the weight sensor112(1) may comprise a force sensing resistor (FSR). The FSR may comprisea resilient material that changes one or more electrical characteristicswhen compressed. For example, the electrical resistance of a particularportion of the FSR may decrease as the particular portion is compressed.The inventory management system 122 may use the data acquired by theweight sensors 112(1) to identify an object, determine a change in thequantity of objects, determine a location of an object, maintainshipping records, and so forth.

The sensors 112 may include capacitive sensors 112(2). As describedabove with regard to FIG. 2, the capacitive sensor 112(2) may compriseone or more conductive elements 206 and the capacitive sensor module212. In some implementations, the capacitive sensor 112(2) may includeor utilize a switch module 210. The capacitive sensor 112(2) may beconfigured to use a far-field capacitance effect that may comprisemeasuring the self-capacitance of the conductive elements 206, ratherthan a mutual capacitance. In one implementation, a fixed charge may beprovided to the conductive element 206, and the resultant voltage may bemeasured between the conductive element 206 and the ground.

In other implementations, the capacitive sensor 112(2) may be configuredto operate in a mutual capacitance mode, surface capacitance mode, andso forth. In mutual capacitance mode, at least two conductive layers arearranged in a stack with a dielectric material between the layers. Thedielectric may be a solid, such as a plastic, a gas such as air, avacuum, and so forth. The mutual capacitance at points between theselayers is measured. When another object touches the outermost conductivelayer, the mutual capacitance between the two layers changes, allowingfor detection. In surface capacitance mode, voltages are applied todifferent points of a conductive element 206 to produce an electrostaticfield. By measuring the changes in current draw (or another electricalcharacteristic) from the different points at which voltage is applied, alocation of an object may be determined.

The sensors 112 may include one or more image sensors 112(3). The one ormore image sensors 112(3) may include imaging sensors configured toacquire images of a scene. The image sensors 112(3) are configured todetect light in one or more wavelengths including, but not limited to,terahertz, infrared, visible, ultraviolet, and so forth. The imagesensors 112(3) may comprise charge coupled devices (CCD), complementarymetal oxide semiconductor (CMOS) devices, microbolometers, and so forth.The inventory management system 122 may use image data 118 acquired bythe image sensors 112(3) during operation of the facility 702. Forexample, the inventory management system 122 may identify items 106,users 108, totes 110, and so forth, based at least in part on theirappearance within the image data 118 acquired by the image sensors112(1). The image sensors 112(3) may be mounted in various locationswithin the facility 702. For example, image sensors 112(3) may bemounted overhead, on inventory locations 712, may be worn or carried byusers 108, may be affixed to totes 110, and so forth.

One or more depth sensors 112(4) may also be included in the sensors112. The depth sensors 112(4) are configured to acquire spatial orthree-dimensional (3D) data, such as depth information, about objectswithin a FOV 120. The depth sensors 112(4) may include range cameras,lidar systems, sonar systems, radar systems, structured light systems,stereo vision systems, optical interferometry systems, and so forth. Theinventory management system 122 may use the 3D data acquired by thedepth sensors 112(4) to identify objects, determine a location of anobject in 3D real space, and so forth.

One or more buttons 112(5) may be configured to accept input from theuser 108. The buttons 112(5) may comprise mechanical, capacitive,optical, or other mechanisms. For example, the buttons 112(5) maycomprise mechanical switches configured to accept an applied force froma touch of the user 108 to generate an input signal. The inventorymanagement system 122 may use data from the buttons 112(5) to receiveinformation from the user 108. For example, the tote 110 may beconfigured with a button 112(5) to accept input from the user 108 andsend information indicative of the input to the inventory managementsystem 122.

The sensors 112 may include one or more touch sensors 112(6). The touchsensors 112(6) may use resistive, capacitive, surface capacitance,projected capacitance, mutual capacitance, optical, InterpolatingForce-Sensitive Resistance (IFSR), or other mechanisms to determine theposition of a touch or near-touch. For example, the IFSR may comprise amaterial configured to change electrical resistance responsive to anapplied force. The location within the material of that change inelectrical resistance may indicate the position of the touch. Theinventory management system 122 may use data from the touch sensors112(6) to receive information from the user 108. For example, the touchsensor 112(6) may be integrated with the tote 110 to provide atouchscreen with which the user 108 may select from a menu one or moreparticular items 106 for picking, enter a manual count of items 106 atan inventory location 712, and so forth.

One or more microphones 112(7) may be configured to acquire informationindicative of sound present in the environment. In some implementations,arrays of microphones 112(7) may be used. These arrays may implementbeamforming techniques to provide for directionality of gain. Theinventory management system 122 may use the one or more microphones112(7) to acquire information from acoustic tags 806, accept voice inputfrom the users 108, determine ambient noise level, and so forth.

The sensors 112 may include one or more optical sensors 112(8). Theoptical sensors 112(8) may be configured to provide data indicative ofone or more of color or intensity of light impinging thereupon. Forexample, the optical sensor 112(8) may comprise a photodiode andassociated circuitry configured to generate a signal or data indicativeof an incident flux of photons. As described below, the optical sensorarray 112(14) may comprise a plurality of the optical sensors 112(8).For example, the optical sensor 112(8) may comprise an array of ambientlight sensors such as the ISL76683 as provided by Intersil Corporationof Milpitas, Calif., USA, or the MAX44009 as provided by MaximIntegrated of San Jose, Calif., USA. In other implementations, otheroptical sensors 112(8) may be used. The optical sensors 112(8) may besensitive to one or more of infrared light, visible light, orultraviolet light. For example, the optical sensors 112(8) may besensitive to infrared light, and infrared light sources such as lightemitting diodes (LEDs) may provide illumination.

The optical sensors 112(8) may include photodiodes, photoresistors,photovoltaic cells, quantum dot photoconductors, bolometers,pyroelectric infrared detectors, and so forth. For example, the opticalsensor 112(8) may use germanium photodiodes to detect infrared light.

One or more radio frequency identification (RFID) readers 112(9), nearfield communication (NFC) systems, and so forth, may be included assensors 112. For example, the RFID readers 112(9) may be configured toread the RF tags 806. Information acquired by the RFID reader 112(9) maybe used by the inventory management system 122 to identify an objectassociated with the RF tag 806 such as the item 106, the user 108, thetote 110, and so forth. For example, based on information from the RFIDreaders 112(9) detecting the RF tag 806 at different times and RFIDreaders 112(9) having different locations in the facility 702, avelocity of the RF tag 806 may be determined.

One or more RF receivers 112(10) may also be included as sensors 112. Insome implementations, the RF receivers 112(10) may be part oftransceiver assemblies. The RF receivers 112(10) may be configured toacquire RF signals 808 associated with Wi-Fi®, Bluetooth®, ZigBee®, 4G,3G, LTE, or other wireless data transmission technologies. The RFreceivers 112(10) may provide information associated with datatransmitted via radio frequencies, signal strength of RF signals 808,and so forth. For example, information from the RF receivers 112(10) maybe used by the inventory management system 122 to determine a locationof an RF source, such as a communication interface onboard the tote 110.

The sensors 112 may include one or more accelerometers 112(11), whichmay be worn or carried by the user 108, mounted to the tote 110, and soforth. The accelerometers 112(11) may provide information such as thedirection and magnitude of an imposed acceleration. Data such as rate ofacceleration, determination of changes in direction, speed, and soforth, may be determined using the accelerometers 112(11).

A gyroscope 112(12) may provide information indicative of rotation of anobject affixed thereto. For example, the tote 110 or other objects maybe equipped with a gyroscope 112(12) to provide data indicative of achange in orientation of the object.

A magnetometer 112(13) may be used to determine an orientation bymeasuring ambient magnetic fields, such as the terrestrial magneticfield. The magnetometer 112(13) may be worn or carried by the user 108,mounted to the tote 110, and so forth. For example, the magnetometer112(13) mounted to the tote 110 may act as a compass and provideinformation indicative of which direction the tote 110 is oriented.

An optical sensor array 112(14) may comprise one or more optical sensors112(8). The optical sensors 112(8) may be arranged in a regular,repeating, or periodic two-dimensional arrangement such as a grid. Theoptical sensor array 112(14) may generate image data 118. For example,the optical sensor array 112(14) may be arranged within or below aninventory location 712 and obtain information about shadows of items106, hand of the user 108, and so forth.

The sensors 112 may include proximity sensors 112(15) used to determinepresence of an object, such as the user 108, the tote 110, and so forth.The proximity sensors 112(15) may use optical, electrical, ultrasonic,electromagnetic, or other techniques to determine a presence of anobject. In some implementations, the proximity sensors 112(15) may usean optical emitter and an optical detector to determine proximity. Forexample, an optical emitter may emit light, a portion of which may thenbe reflected by the object back to the optical detector to provide anindication that the object is proximate to the proximity sensor 112(15).In other implementations, the proximity sensors 112(15) may comprise acapacitive proximity sensor 112(15) configured to provide an electricalfield and determine a change in electrical capacitance due to presenceor absence of an object within the electrical field.

The proximity sensors 112(15) may be configured to provide sensor dataindicative of one or more of a presence or absence of an object, adistance to the object, or characteristics of the object. An opticalproximity sensor 112(15) may use time-of-flight (ToF), structured light,interferometry, or other techniques to generate the distance data. Forexample, ToF determines a propagation time (or “round-trip” time) of apulse of emitted light from an optical emitter or illuminator that isreflected or otherwise returned to an optical detector. By dividing thepropagation time in half and multiplying the result by the speed oflight in air, the distance to an object may be determined. In anotherimplementation, a structured light pattern may be provided by theoptical emitter. A portion of the structured light pattern may then bedetected on the object using a sensor 112 such as an image sensor112(3). Based on an apparent distance between the features of thestructured light pattern, the distance to the object may be calculated.Other techniques may also be used to determine distance to the object.In another example, the color of the reflected light may be used tocharacterize the object, such as skin, clothing, tote 110, and so forth.

The sensors 112 may also include an instrumented auto-facing unit (IAFU)112(16). The IAFU 112(16) may comprise a position sensor configured toprovide data indicative of displacement of a pusher. As an item 106 isremoved from the IAFU 112(16), the pusher moves, such as under theinfluence of a spring, and pushes the remaining items 106 in the IAFU112(16) to the front of the inventory location 712. By using data fromthe position sensor, and given item data 126 such as a depth of anindividual item 106, a count may be determined, based on a change inposition data. For example, if each item 106 is 1 inch deep, and theposition data indicates a change of 8 inches, the quantity held by theIAFU 112(16) may have changed by 8 items 106. This count information maybe used to confirm or provide a cross check for a count obtained byother means, such as analysis of the weight data 114, the capacitancedata 116, the image data 118, and so forth.

The sensors 112 may include other sensors 112(S) as well. For example,the other sensors 112(S) may include light curtains, ultrasonicrangefinders, thermometers, barometric sensors, air pressure sensors,hygrometers, and so forth. For example, the inventory management system122 may use information acquired from thermometers and hygrometers inthe facility 702 to direct the user 108 to check on delicate items 106stored in a particular inventory location 712, which is overheating, toodry, too damp, and so forth.

In one implementation, a light curtain may utilize a linear array oflight emitters and a corresponding linear array of light detectors. Forexample, the light emitters may comprise a line of infrared LEDs orvertical cavity surface emitting lasers (VCSELs) that are arranged abovea top shelf 102 in front of the inventory location 712, while the lightdetectors comprise a line of photodiodes sensitive to infrared lightarranged below the light emitters. The light emitters produce a“lightplane” or sheet of infrared light that is then detected by thelight detectors. An object passing through the lightplane may decreasethe amount of light falling upon the light detectors. For example, theuser's 108 hand would prevent at least some of the light from lightemitters from reaching a corresponding light detector. As a result, aposition along the linear array of the object may be determined that isindicative of a touchpoint. This position may be expressed as touchpointdata, with the touchpoint being indicative of the intersection betweenthe hand of the user 108 and the sheet of infrared light. In someimplementations, a pair of light curtains may be arranged at rightangles relative to one another to provide two-dimensional touchpointdata indicative of a position of touch in a plane. Input from the lightcurtain, such as indicating occlusion from a hand of a user 108 may beused to generate event data 506.

In some implementations, the image sensor 112(3) or other sensors 112(S)may include hardware processors, memory, and other elements configuredto perform various functions. For example, the image sensors 112(3) maybe configured to generate image data 118, send the image data 118 toanother device such as the server 804, and so forth.

The facility 702 may include one or more access points 810 configured toestablish one or more wireless networks. The access points 810 may useWi-Fi®, NFC, Bluetooth®, or other technologies to establish wirelesscommunications between a device and the network 802. The wirelessnetworks allow the devices to communicate with one or more of thesensors 112, the inventory management system 122, the optical sensorarrays 112(14), the tag 806, a communication device of the tote 110, orother devices.

Output devices 812 may also be provided in the facility 702. The outputdevices 812 are configured to generate signals, which may be perceivedby the user 108 or detected by the sensors 112. In some implementations,the output devices 812 may be used to provide illumination of theoptical sensor array 112(14).

Haptic output devices 812(1) are configured to provide a signal thatresults in a tactile sensation to the user 108. The haptic outputdevices 812(1) may use one or more mechanisms such as electricalstimulation or mechanical displacement to provide the signal. Forexample, the haptic output devices 812(1) may be configured to generatea modulated electrical signal, which produces an apparent tactilesensation in one or more fingers of the user 108. In another example,the haptic output devices 812(1) may comprise piezoelectric or rotarymotor devices configured to provide a vibration, which may be felt bythe user 108.

One or more audio output devices 812(2) may be configured to provideacoustic output. The acoustic output includes one or more of infrasonicsound, audible sound, or ultrasonic sound. The audio output devices812(2) may use one or more mechanisms to generate the acoustic output.These mechanisms may include, but are not limited to, the following:voice coils, piezoelectric elements, magnetorestrictive elements,electrostatic elements, and so forth. For example, a piezoelectricbuzzer or a speaker may be used to provide acoustic output.

The display devices 812(3) may be configured to provide output, whichmay be seen by the user 108 or detected by a light-sensitive sensor suchas an image sensor 112(3) or an optical sensor 112(8). In someimplementations, the display devices 812(3) may be configured to produceoutput in one or more of infrared, visible, or ultraviolet light. Theoutput may be monochrome or in color. The display devices 812(3) may beone or more of emissive, reflective, microelectromechanical, and soforth. An emissive display device 812(3), such as using LEDs, isconfigured to emit light during operation. In comparison, a reflectivedisplay device 812(3), such as using an electrophoretic element, relieson ambient light to present an image. Backlights or front lights may beused to illuminate non-emissive display devices 812(3) to providevisibility of the output in conditions where the ambient light levelsare low.

The display devices 812(3) may be located at various points within thefacility 702. For example, the addressable displays may be located oninventory locations 712, totes 110, on the floor of the facility 702,and so forth.

Other output devices 812(P) may also be present. For example, the otheroutput devices 812(P) may include scent/odor dispensers, documentprinters, 3D printers or fabrication equipment, and so forth.

FIG. 9 illustrates a block diagram 900 of a server 804 configured tosupport operation of the facility 702, according to someimplementations. The server 804 may be physically present at thefacility 702, may be accessible by the network 802, or a combination ofboth. The server 804 does not require end-user knowledge of the physicallocation and configuration of the system that delivers the services.Common expressions associated with the server 804 may include “on-demandcomputing”, “software as a service (SaaS)”, “platform computing”,“network-accessible platform”, “cloud services”, “data centers”, and soforth. Services provided by the server 804 may be distributed across oneor more physical or virtual devices.

One or more power supplies 902 may be configured to provide electricalpower suitable for operating the components in the server 804. The oneor more power supplies 902 may comprise batteries, capacitors, fuelcells, photovoltaic cells, wireless power receivers, conductivecouplings suitable for attachment to an external power source such asprovided by an electric utility, and so forth. The server 804 mayinclude one or more hardware processors 904 (processors) configured toexecute one or more stored instructions. The processors 904 may compriseone or more cores. One or more clocks 906 may provide informationindicative of date, time, ticks, and so forth. For example, theprocessor 904 may use data from the clock 906 to associate a particularinteraction with a particular point in time.

The server 804 may include one or more communication interfaces 908 suchas input/output (I/O) interfaces 910, network interfaces 912, and soforth. The communication interfaces 908 enable the server 804, orcomponents thereof, to communicate with other devices or components. Thecommunication interfaces 908 may include one or more I/O interfaces 910.The I/O interfaces 910 may comprise Inter-Integrated Circuit (I2C),Serial Peripheral Interface bus (SPI), Universal Serial Bus (USB) aspromulgated by the USB Implementers Forum, RS-232, and so forth.

The I/O interface(s) 910 may couple to one or more I/O devices 914. TheI/O devices 914 may include input devices such as one or more of asensor 112, keyboard, mouse, scanner, and so forth. The I/O devices 914may also include output devices 812 such as one or more of a displaydevice 812(3), printer, audio speakers, and so forth. In someembodiments, the I/O devices 914 may be physically incorporated with theserver 804 or may be externally placed.

The network interfaces 912 may be configured to provide communicationsbetween the server 804 and other devices, such as the totes 110,routers, access points 810, and so forth. The network interfaces 912 mayinclude devices configured to couple to personal area networks (PANs),local area networks (LANs), wireless local area networks (WLANS), widearea networks (WANs), and so forth. For example, the network interfaces912 may include devices compatible with Ethernet, Wi-Fi®, Bluetooth®,ZigBee®, and so forth.

The server 804 may also include one or more busses or other internalcommunications hardware or software that allow for the transfer of databetween the various modules and components of the server 804.

As shown in FIG. 9, the server 804 includes one or more memories 916.The memory 916 may comprise one or more non-transitory computer-readablestorage media (CRSM). The CRSM may be any one or more of an electronicstorage medium, a magnetic storage medium, an optical storage medium, aquantum storage medium, a mechanical computer storage medium, and soforth. The memory 916 provides storage of computer-readableinstructions, data structures, program modules, and other data for theoperation of the server 804. A few example functional modules are shownstored in the memory 916, although the same functionality mayalternatively be implemented in hardware, firmware, or as a system on achip (SoC).

The memory 916 may include at least one operating system (OS) module918. The OS module 918 is configured to manage hardware resource devicessuch as the I/O interfaces 910, the I/O devices 914, the communicationinterfaces 908, and provide various services to applications or modulesexecuting on the processors 904. The OS module 918 may implement avariant of the FreeBSD™ operating system as promulgated by the FreeBSDProject; other UNIX™ or UNIX-like variants; a variation of the Linux™operating system as promulgated by Linus Torvalds; the Windows®operating system from Microsoft Corporation of Redmond, Wash., USA; andso forth.

Also stored in the memory 916 may be a data store 920 and one or more ofthe following modules. These modules may be executed as foregroundapplications, background tasks, daemons, and so forth. The data store920 may use a flat file, database, linked list, tree, executable code,script, or other data structure to store information. In someimplementations, the data store 920 or a portion of the data store 920may be distributed across one or more other devices including theservers 804, network attached storage devices, and so forth.

A communication module 922 may be configured to establish communicationswith one or more of the totes 110, sensors 112, display devices 812(3),other servers 804, or other devices. The communications may beauthenticated, encrypted, and so forth.

The memory 916 may store an inventory management module 924. Theinventory management module 924 is configured to provide the inventoryfunctions as described herein with regard to the inventory managementsystem 122. For example, the inventory management module 924 may trackitems 106 between different inventory locations 712, to and from thetotes 110, and so forth.

The inventory management module 924 may include one or more of a dataacquisition module 926, the analysis module 124, an item tracking module928, an action module 930, and so forth. The data acquisition module 926may be configured to acquire and access information associated withoperation of the facility 702. For example, the data acquisition module926 may be configured to acquire sensor data 932 such as the weight data114, capacitance data 116, image data 118, image data 118, and so forth.The sensor data 932 may be accessed by the other modules for use.

As described above, the analysis module 124 may be configured togenerate the interaction data 128 using the sensor data 932, item data126, physical layout data 934, and so forth.

The item tracking module 928 may access physical layout data 934 andgenerate account item data 936. The item tracking module 928 may beconfigured to determine a location within the facility 702, a user 108,a user account, and so forth, that is associated with one or more items106. For example, the item tracking module 928 may determine that anitem 106 has been removed from lane 104(1) and placed into the tote 110.The item tracking module 928 may then determine that the tote 110 isassociated with the user 108 or the user account that represents theuser 108. The item tracking module 928 may also use the interaction data128. For example, the interaction data 128 that is indicative of aparticular type of item 106 being removed from a particular inventorylocation 712 may be used as part of the input to track the items 106that are in the custody of a particular user 108.

The inventory management system 122 may utilize the physical layout data934. The physical layout data 934 may provide information indicative ofwhere sensors 112 and inventory locations 712 are in the facility 702with respect to one another, FOV 120 of sensors 112 relative to theinventory location 712, and so forth. For example, the physical layoutdata 934 may comprise information representative of a map or floor planof the facility 702 with relative positions of inventory locations 712,planogram data indicative of how items 106 are to be arranged at theinventory locations 712, and so forth.

The physical layout data 934 may associate a particular inventorylocation ID with other information such as physical location data,sensor position data, sensor direction data, sensor identifiers, and soforth. The physical location data provides information about where inthe facility 702 objects are, such as the inventory location 712, thesensors 112, and so forth. In some implementations, the physicallocation data may be relative to another object. For example, thephysical location data may indicate that a particular weight sensor112(1), capacitive sensor 112(2), or image sensor 112(3) is associatedwith the lane 104(1).

The inventory management module 924 may utilize this information duringoperation. For example, the item tracking module 928 may utilizephysical layout data 934 to determine what capacitance data 116 acquiredfrom particular capacitive sensors 112(2) corresponds to a particularshelf 102, lane 104, or other inventory location 712.

The item tracking module 928 may access information from sensors 112within the facility 702, such as those at the shelf 102 or otherinventory location 712, onboard the tote 110 or carried by or worn bythe user 108. For example, the item tracking module 928 may receiveinformation from an RFID reader at the shelf 102 that is indicative oftags associated with each of the items 106 that are placed onto theshelf 102.

The account item data 936 may also be included in the data store 920 andcomprises information indicative of one or more items 106 that arewithin the custody of a particular user 108, within a particular tote110, and so forth. For example, the account item data 936 may comprise alist of the contents of the tote 110. Continuing the example, the listmay be further associated with the user account representative of theuser 108. In another example, the account item data 936 may comprise alist of items 106 that the user 108 is carrying. The item trackingmodule 928 may use the account item data 936 to determine subsets ofpossible items 106 with which the user 108 may have interacted.

The inventory management module 924, and modules associated therewith,may access sensor data 932, threshold data 938, and so forth. Thethreshold data 938 may comprise one or more thresholds, ranges,percentages, and so forth, that may be used by the various modules inoperation. For example, the event detection module 602 may accessthreshold data 938 to determine event data 506. The threshold data 938may be determined by analyzing sensor data 932 to determine the sensorvalues obtained when no event has been determined. For example, thecapacitance data 116 obtained during a period of time in which no users108 are present in the facility 702 may be used to generate a baselinecapacitance value. A factor may be applied to the baseline capacitancevalue to determine a threshold capacitance value used to determineoccurrence of an event. Continuing the example, the factor may be 20%,so the baseline capacitance value may be multiplied by 1.2 to producethe threshold value. A change in capacitance value that exceeds thisthreshold may thus result in the event detection module 602 generatingevent data 506 indicative of an event. The data processing module 604may access threshold data 938 during the generation of processed data.The interaction data module 616 may access the threshold data 938 duringthe generation of the location data 620, the interaction data 128, andso forth.

The inventory management module 924 may generate output data 940. Forexample, the output data 940 may include the interaction data 128,inventory levels for individual types of items 106, overall inventory,and so forth.

The action module 930 may be configured to initiate or coordinate one ormore actions responsive to output data 940. For example, the actionmodule 930 may access output data 940 that indicates a particularinventory location 712 is empty and in need of restocking. An actionsuch as a dispatch of a work order or transmitting instructions to arobot may be performed to facilitate restocking of the inventorylocation 712.

Processing sensor data 932, such as the image data 118, may be performedby a module implementing, at least in part, one or more of the followingtools or techniques. In one implementation, processing of the image data118 may be performed, at least in part, using one or more toolsavailable in the OpenCV library as developed by Intel Corporation ofSanta Clara, Calif., USA; Willow Garage of Menlo Park, Calif., USA; andItseez of Nizhny Novgorod, Russia, with information available atwww.opencv.org. In another implementation, functions available in theOKAO machine vision library as promulgated by Omron Corporation ofKyoto, Japan, may be used to process the sensor data 932. In stillanother implementation, functions such as those in the Machine VisionToolbox for Matlab (MVTB) available using MATLAB as developed byMathWorks, Inc. of Natick, Mass., USA, may be utilized.

Techniques such as artificial neural networks (ANNs), active appearancemodels (AAMs), active shape models (ASMs), principal component analysis(PCA), cascade classifiers, and so forth, may also be used to processthe sensor data 932 or other data. For example, the ANN may be a trainedusing a supervised learning algorithm such that object identifiers areassociated with images of particular objects within training imagesprovided to the ANN. Once trained, the ANN may be provided with thesensor data 932 and the item data 126 to allow for a determination ofsimilarity between two or more images.

In some implementations, the item tracking module 928 may process imagedata 118 using one or more machine vision counting techniques todetermine a count of the items 106. For example, machine vision countingtechniques may be configured to recognize a top or front portion of theitems 106 in the image data 118. This determination may be based on itemdata 126, such as previously acquired images of a sampled item 106. Eachof the tops of the type of item 106 appearing in the image data 118 maybe identified and a count made. A change in count may be determinedbased on image data 118 obtained at a first time and a second time,respectively.

In one implementation, the item tracking module 928 may use one or morealgorithms to determine the items 106 in the FOV 120. For example, ahistogram of gradients (HOG) algorithm may be used to extract thefeatures of the items 106. A state vector machine (SVM) may then be usedto classify the extracted features and determine which of the extractedfeatures correspond to items 106. Output data 940 may be generated thatis the resulting count of the items 106 determined by the SVM to be inthe image data 118.

Other modules 942 may also be present in the memory 916 as well as otherdata 944 in the data store 920. For example, the other modules 942 mayinclude an accounting module while the other data 944 may includebilling data. The accounting module may be configured to assess chargesto accounts associated with particular users 108 or other entities,while the billing data may include information such as payment accountnumbers. In another example, the other modules 942 may include a seeddistribution module. The seed distribution module may access physicallayout data 934 and distribute seed value 402 to the devices controllingthe capacitive sensors 112(2) such that the seed value 402 in use byadjacent devices are different. The seed distribution module maygenerate seed value 402, or retrieve previously generated seed value402.

Illustrative Processes

FIG. 10 depicts a flow diagram of a process 1000 for determining alocation of an interaction at an inventory location 712, according tosome implementations. The process 1000 may be implemented at least inpart by one or more of a computing device at the inventory location 712,such as a shelf controller, by the server 804, or by another computingdevice.

At 1002, a first type of item 106 associated with the shelf 102 isdetermined. For example, the item data 126 may be accessed to determinethe types of items 106 that are stowed at the particular shelf 102. Inanother example, an RFID interrogation signal may be sent at the shelf102, and the resulting response may be used to determine the type ofitem 106.

At 1004, item data 126 associated with the first type of item 106 isdetermined. For example, the item data 126 may comprise geometry datasuch as information about the width and depth of the item 106,information about how the item 106 affects capacitance at a capacitivesensor 112(2), and so forth.

At 1006, a first set of capacitance values is determined at a first timefrom an array of capacitive sensors 112(2). For example, the capacitivesensors 112(2) may have conductive elements 206 arranged as described inFIG. 3. Each capacitance value may be indicative of capacitance at aparticular capacitive sensor 112(2) or the conductive element 206 ofthat capacitive sensor 112(2). In some implementations, the first set ofcapacitance values may comprise baseline values for individual ones ofthe conductive elements 206.

At 1008, a second set of capacitance values is determined at a secondtime from the array of capacitive sensors 112(2). Each capacitance valueof the second set may indicate capacitance at the particular capacitivesensor 112(2) used to generate the first set of capacitance values. Forexamples, at a later time, the second set of capacitance values may beread out from the array of capacitive sensors 112(2).

At 1010, change values are determined by subtracting capacitance valuesin the first set from capacitance values in the second set, for eachparticular capacitive sensor 112(2). The change values are indicative ofa change in capacitive over time.

At 1012, a top “k” set of the change values as sorted in descendingorder of greatest magnitude to least magnitude is determined, where k isa positive integer. For example, where k equals four, the four changevalues having the greatest absolute value may be selected. In someimplementations, the top k set may include as few as one change value.The top k set of change values may be selected based on absolute valueof difference, actual signed difference, percentage change, using astepwise function, and so forth. In other implementations, other sortsmay be used. For example, the change values may be sorted in ascendingorder of least magnitude to greatest magnitude, and the “top k” set maycomprise the “bottom k” set of the change values.

At 1014, a set of “n” change values from the top k set that are greaterthan a threshold value is determined. For example, those change valuesin the top k set that are less than or equal to the threshold value maybe omitted from the set of n change values. In some implementations, theset of n change values may comprise a single value.

In one implementation, the threshold value may be determined by applyinga predetermined factor to the change value that exhibits the greatestvalue. For example, the change values may be sorted in descending order,greatest to smallest. The change value that is greatest may bemultiplied by a predetermined factor (such as 0.20) to produce thethreshold value. Continuing this example, change values that are lessthan 20% of the greatest value may be omitted from the set of n changevalues.

At 1016, a first estimated location relative to the array is determinedusing the set of n change values. In some implementations, the item data126 may also be used to determine the first estimated location. The setof n change values and item data 126 such as dimensions, effects oncapacitance, and so forth, may be used as input to a minimum mean squareerror estimation (MMSE) function, a Kalman filter or linear quadraticestimation, and so forth.

In some implementations, the change value of capacitance (C_(delta)) maybe expressed using the following linear equation:

$\begin{matrix}{C_{delta} = {a + {b*\sqrt[2]{\left( {x - {x\; 0}} \right)^{2} + \left( {y - {y\; 0}} \right)^{2}}}}} & {{Equation}\mspace{14mu} 1}\end{matrix}$

In Equation 1, the variables a and b are indicative of attributes of aparticular type of item 106, such as the dimensions, effects oncapacitance of that type of item 106, and so forth. The value of thevariable x0 may be indicative of the coordinates along an x-axis of acentroid of a conductive element 206 used to obtain the capacitance data116, and the value of the variable x is indicative of the coordinates ofthe item 106 or other object that is being detected. Likewise, thevariables y0 and y are indicative of the coordinates along a y-axis(perpendicular to the x-axis) of a centroid of the conductive element206 and the object, respectively.

An equation or other expression that relates distance from theconductive element 206 and an object may be determined experimentally bytesting a representative inventory location 712 that is equipped withone or more capacitive sensors 112(2) and a using a known target. Forexample:C_(delta)=a * e^((lambda*d))

In Equation 2, as described above, the variable a is indicative of oneor more attributes of a particular type of item 106. Lambda may bedetermined experimentally while the variable d is indicative of adistance from a capacitive center of the item 106 to the capacitivecenter of the conductive element 206. For example, the capacitive centerof a torus of homogenous material is located in the free space of thecenter of the torus.

In the implementation utilizing a minimum mean square error (MMSE)function with the set of n change values, several possible cases may bepresented. These cases are described next.

A first case involves the situation where the only change values thatexceed the threshold value are obtained from a single capacitive sensor112(2) or the conductive element 206 associated therewith. In thissituation, the estimated location is the location of that capacitivesensor 112(2) or the conductive element 206.

A second case occurs when the number of capacitive sensors 112(2) (orthe associated conductive elements 206) that produce change valuesexceeding the threshold value is greater than or equal to two. In thissituation, the first estimated location may be determined as:

(x, y) = Min{Σ(C_(delta_(i)) * e^((lambda * d_(i))) − C_(delta₁) * e^((lambda * d₁)))²}${{{where}\mspace{20mu} i} = {2\mspace{14mu}{to}\mspace{14mu} n}},{d_{1} = \sqrt[2]{\left( {x - {x\; 1}} \right)^{2} + \left( {y + {y\; 1}} \right)^{2}}}$$d_{i} = {\sqrt[2]{\left( {x - {x\; i}} \right)^{2} + \left( {y + {y\; i}} \right)^{2}}\mspace{14mu}{and}}$

where i=2 to n,d ₁=²√{square root over ((x−x1)²+(y−y1)²)}d _(i)=²√{square root over ((x−xi)²+(y−yi)²)} and

(xi, yi) is indicative of the location of the i^(th) capacitive sensor112(2) or conductive element 206 that provided a change value greaterthan the threshold value.

Equation 3

At 1018, a second estimated location is determined based on sensor data932 acquired using one or more other sensors 112. In one example, asecond estimated location is determined based on weight data 114acquired using the plurality of weight sensors 112(1). Each weight valuemay be indicative of a weight at a particular weight sensor 112(1). At afirst time, a first set of weight values from the plurality of weightsensors 112(1) may be determined. At a second time, a second set ofweight values from the plurality of weight sensors 112(1) may bedetermined. Weight change values may be generated based on the first setof weight values and the second set of weight values. A center of weightchange location may be determined based on the weight change values. Forexample, given the relative positions and distances of the weightsensors 112(1) and the respective weight changes measured at each of therelative positions of the weight sensors 112(1), a center of weight (orcenter of mass) change may be determined. For example, the secondestimated location may be indicative of a location on the shelf 102associated with the change in the center of weight. A location of thecenter of weight may be designated as the second estimated location. Inanother example, a center of weight based on a first set of weightvalues acquired at a first time and a second set of weight valuesacquired at a second time may be calculated.

Weight change data is indicative of a change in weight as measured byone or more of the weight sensors 112(1) from a first time to a secondtime. For example, calculation of the weight change data may comprisesubtracting a first weight obtained at the first time from the secondweight obtained at the second time. In some implementations, theinventory management module 924 may determine the weight change data. Inother implementations, the determination of the weight change data maybe performed at least partially onboard the weight sensor 112(1) or anassociated device such as a controller. In some implementations, theweight change data may include information indicative of noise in theweight data 114, variability of the weight data 114, estimatedreliability of the weight data 114, and so forth.

Weight distribution data may provide data indicative of weightdistribution at a particular time. The weight distribution data may beexpressed as a measured weight at a particular weight sensor 112(1), aratio or percentage of weight on a weight sensor 112(1), and so forth.For example, the weight distribution data may be expressed as “3213 gleft, 2214 g right”, “0.59 left, 0.41 right”, and so forth. In someimplementations, the inventory management module 924 may determine theweight distribution data. In other implementations, the determination ofthe weight distribution data may be performed at least partially onboardthe weight sensor 112(1) or an associated device such as a controller.The weight distribution data may indicate a change in the weightdistribution from a first time to a second time. The weight distributiondata may be expressed as a weight associated with one or more of theweight sensors 112(1). For example, the weight distribution data for aconfiguration in which a rectangular shelf has a weight sensor 112(1) ateach of the four corners may have weight distribution data correspondingto each of the corners. In another example, data from weight sensors112(1) may be combined, such as to provide a weight measured at a leftside of the inventory location 712 and a weight measured at a right sideof the inventory location 712. In some implementations, the inventorymanagement module 924 may determine the weight distribution data. Inother implementations, the determination of the weight distribution datamay be performed at least partially on board the weight sensor 112(1) oran associated device such as a controller. The weight distribution datamay provide data indicative of center of weight (COW) at a particulartime. For example, the weight distribution data may indicate a COW,change in the COW from a first time to a second time, and so forth.

A variety of techniques may be used to calculate the COW or center ofgravity. The COW may be described as a point in space at which weightedposition vectors relative to the point sum to zero. For example, the COWof a uniform sphere is a point in the center of the sphere. In anotherexample, the COW of a toroid is a point is the center of the toroid. Avariety of techniques may be used to calculate the COW. Consider asimple system having two masses, m1 and m2, arranged along a single axis“x” at positions x1 and x2, respectively. The position of each mass isgiven as a distance “x” relative to an origin. The COW may be expressedby the equation:x=((m1*x1)+(m2*x2))/(m1+m2)  Equation 4

The physical characteristics of the inventory location 712, placement ofthe weight sensors 112(1) at the inventory location 712, physicalposition of the lane 104 relative to the inventory location 712,quantity and weight of the items 104 at the respective lanes 104, and soforth, may be known. For example, given the physical design of theinventory location 712, it may be known that a weight sensor 112(1) ispositioned at each of the four corners of a shelf 102, and that theshelf 102 has a particular length and width. Continuing the example,based on the physical design of the inventory location 712, the physicalcoordinates corresponding to the lane 104 on that shelf 102 are known.Using this information, as well as the item data 136, weightcharacteristic data may be generated for an inventory location 712before, during, or after an interaction.

Location of weight change (LWC) data provides information indicative ofthe location, with respect to the inventory location 712, at which aweight change has taken place. For example, the LWC data may indicatethat a weight change has taken place at 15 cm from the origin of theinventory location 712. The LWC data may be determined using the weightdata 114. For example, the LWC data may be calculated from the weightdistribution data.

In some implementations, the LWC data may be expressed as a vector valuehaving a direction and a magnitude. For example, the LWC data maycomprise a vector having a first endpoint at an origin of the inventorylocation 712 and a second endpoint at the LWC.

In one implementation, the LWC data may be determined as follows. Assumea situation wherein the inventory location 712 comprises a shelf 102having a width “a”, a left weight sensor 112(1) located at a distance“b” from the left edge of the shelf 102 and right weight sensor 112(1)located at a distance “b” from the right edge of the shelf 102. Theweight measured by the left weight sensor 112(1) is “w1” and the weightmeasured by the right weight sensor 112(1) is “w2”. A distance “LWC”indicative of the location of weight change from an origin at theleftmost edge of the shelf 102 may be calculated to the center-of-massof an individual item 104 that has been added or removed in aninteraction using the following equation:LWC=w2*(a−2b)/(w2+w1)+b  Equation 5

The weight change corresponding to the interaction may be calculated as:Total weight change=w1+w2  Equation 6

During operation, the weight data 114 may be “tared” or zeroed out whilethe load on the platform measured by the weight sensors 112(1) is in astable state. Subsequent changes in the weight data 114 may be used toproduce the weight distribution data. For example, the inventorylocation 712 when fully loaded may have a total weight of 15 kg. Theprocessing module may “tare” these values, such that the weight is readto be “0 kg”. A subsequent interaction such as a removal of two items106 may result in a total weight change of 910 grams, with a weightdistribution of 850 g on the left and 55 g on the right. Given a shelfwidth “a” of 1 m and the distance “b” of 0.1 m, the LWC is at 0.148meters from the origin at the leftmost edge of the shelf 102.

In another implementation, a second estimated location is determinedbased on image data 118 acquired using the image sensor 112(3) having aFOV 120 that includes at least a portion of the shelf 102. For example,a change between images in the image data 118 acquired at a first timeand a second time may be used to determine a location of an object suchas a hand of the user 108. The second estimated location may bedetermined by mapping coordinates in the image associated with theportion of the hand with a particular location on the shelf 102.Continuing the example, a box bounded by image coordinates (row, column)of (120, 73) and (200, 140) may correspond to a physical locationcoordinates (x, y) of (3 inches, 4 inches).

At 1020, a third estimated location is determined based on the firstestimated location and the second estimated location. For example, thethird estimated location may comprise a location that is midway betweenthe first estimated location and the second estimated location. In someimplementations, a weighted average location may be calculated. Weightsused in this determination may be indicative of the relative degree ofprecision and accuracy associated with the particular sensors 112 orlocation methodology that is used.

At 1022, interaction data 128 is generated that is indicative of aninteraction at the third estimated location. For example, a particulartype of item 106 may be determined based on the third estimatedlocation. A change in weight indicated by the weight data 114 or achange in the count of items 106 determined using image data 118 may beused to determine the change in quantity associated with the particulartype of item 106. The interaction data 128 may then indicate that theparticular type of item 106 experienced a particular change in quantityat the specified inventory location 712.

FIG. 11 depicts a flow diagram of another process 1100 for determining alocation of an interaction at an inventory location 712, according tosome implementations. The process 1100 may be implemented at least inpart by one or more of a computing device at the inventory location 712,such as a shelf controller, by the server 804, or by another computingdevice.

At 1102, a first set of capacitance values measured by the array ofcapacitive sensors 112(1) is determined at a first time. Eachcapacitance value indicates a capacitance value as measured by aparticular capacitive sensor 112(1) at the first time. In someimplementations, the first set of capacitance values may becontemporaneous but not obtained at exactly the same time. For example,as the conductive elements 206 are scanned in accord with the sequencedata 408, the individual capacitance values may be obtained sequentiallywithin a particular interval of time. The first set of capacitancevalues may comprise baseline values for individual ones of theconductive elements 206. In some implementations, each of the baselinevalues for a particular conductive element 206 may be determined fromone or more measurements made using that particular conductive element206.

At 1104, a second set of capacitance values measured by the array ofcapacitive sensors 112(1) is determined at a second time. Each of thecapacitance values indicates a capacitance value as measured by theparticular capacitive sensor 112(2). For example, the same capacitivesensor 112(2) that produces a capacitance value that is in the first setof capacitance values may then be used to produce a capacitance valuethat is in the second set of capacitance values. In someimplementations, each of the second set of values for a particularconductive element 206 may be determined from one or more measurementsmade using that particular conductive element 206.

At 1106, one or more change values based on the capacitance values inthe first set and the capacitance values in the second set aredetermined. For example, individual capacitance values in the first setmay be subtracted from the individual capacitance values in the secondset that are associated with the same conductive element 206.

At 1108, a first estimated location is determined using at least aportion of the one or more change values. For example, the change valuesmay be processed using a MMSE function as described above, a linearquadratic estimation, and so forth. As described above, in someimplementations, the first estimated location may be determined usingitem data 126. For example, the item data 126 may be indicative of awidth and depth of the type of item 106 and a composition of the type ofitem 106. The estimated location based on the change values may beconstrained based on the width and depth of the type of item 106. Forexample, a type of item 106 that exhibits an area that is less than thesize of a conductive element 206 would not be expected to have anestimated location that spans three or more conductive elements 206. Inanother implementation, the width and depth of the type of the item 106and composition may be used to determine an expected capacitance due tothe presence of the item 106 at a location. This expected capacitancemay then be used to determine an area on the shelf 102 that the firstestimated location encompasses, with that area corresponding to the areadescribed by the width and depth of the type of item 106.

At 1110, interaction data 128 is generated indicative of an interactionat the inventory location 712 or portion thereof associated with thefirst estimated location. For example, based on the estimated location,a particular type of item 106 may be identified. Based on sensor data932 such as weight data 114, capacitance data 116, image data 118, andso forth, a quantity of the items 106 at the inventory location 712 maybe determined.

In some implementations, the first estimated location data may beadjusted or snapped to a particular predefined location. For example,data indicative of one or more predetermined lanes 104 at the inventorylocation 712 may be accessed in the physical layout data 934. Aparticular lane 104 may be determined that is one or more of: closest tothe first estimated location or encompasses the first estimatedlocation. For example, the first estimated location may be within theboundaries of the particular lane 104. The interaction data 128 may thenbe associated with the particular lane 104.

In some implementations, additional sensor data may be used to determinea second estimated location. The second estimated location may be usedalong with the first estimated location to determine a third estimatedlocation.

Returning to 1108, many different techniques may be used to determinethe first estimated location using at least a portion of the one or morechange values. In one implementation depicted here, at 1112, model datacomprising one or more models is accessed. Model data may comprisedifferent models, each model representative of a particularconfiguration of capacitance change values and the location that each isassociated with. In some implementations, the model data may begenerated in advance such as during set up for configuration of theinventory location 712.

In another implementation, the determination of the first estimatedlocation may use item data 126. One or more types of items 106associated with the inventory location 712 may be determined. Forexample, the types of items 106 designated as being stowed a particularinventory location 712 may be retrieved. The item data 126 associatedwith these one or more types of items 106 may then be accessed. Thedetermination of the first estimated location may then use the item data126 associated with these one or more types of items 106. Continuing theexample, the geometry data indicative of a width and depth of aparticular type of item 106, information about the effect of that typeof item 106 on capacitance, and so forth, may be used by the locationestimation module 618 to generate location data 620.

In some implementations, information about items 106 that are proximateto the inventory location 712 may be used to generate location data 620.For example, the account item data 936 that is indicative of the one ormore types of items 106 that are within the custody of particular user108 or other agent such a robot may be accessed. The items 106 that arein custody of the user 108 may be stowed in the tote 110, carried, andso forth. When the user 108, other agent, the tote 110, and so forth,are within a threshold distance of the inventory location 712, they maybe deemed to be proximate. The item data 126 associated with these typesof items 106 that are proximate to the inventory location 712 may beaccessed. The item data 126 for those types of items 106 that areproximate may then be used to determine the first estimated location.For example, different model data associated with different types ofitems 106 that are present within the account item data 936 may beassessed with regard to the actual capacitance data 116.

At 1114, at least a portion of the change values are determined to fitone of the one or more models within a threshold value. For example, acurve fitting technique may be used to fit the change values to thedifferent models, and the model that exhibits a least variance betweenthe change values and model values may be determined to be a best fit.In another example, a correspondence between the one or more capacitancechange values and one of the one or more models may be determined to bewithin a threshold value. For example, the threshold value may specify10%, a first model may indicate a capacitance change value of 15 pF.Continuing the example, a second model may indicate a capacitance changevalue of 27 pF, and the capacitance change value measured by thecapacitive sensor 112(1) may be 16 pF. Based on this, a correspondencemay be determined between the first model and the capacitance changevalue.

At 1116, the first estimated location is determined based on a locationassociated with the one or more models. Continuing the example above, alocation associated with the model that exhibits the least variancebetween the change values and the model values may be deemed to be theestimated location.

A calibration operation may be incorporated into the process. At 1118,calibration data 622 may be generated by using the first estimatedlocation obtained, as described above, using the capacitance values andthe second estimated location obtained using other sensor data. Thecalibration data 622 may be indicative of a differential between thefirst estimated location and the second estimated location. For example,a second estimated location may be determined based on image data 118acquired using the image sensor 112(3). Calibration data 622 indicativeof a difference between the first estimated location and the secondestimated location may then be determined. In other implementations, thecalibration data 622 may be indicative of a differential between thefirst estimated location and information indicative of a specifiedlocation. For example, the human operator may manually enter informationindicative of a location, a predefined test target at a known locationmay be used, and so forth.

FIG. 12 depicts a flow diagram of a process 1200 for generating andoperating conductive elements 206 in a capacitive sensor 112(2),according to some implementations. The process 1200 may be implementedat least in part by one or more of a computing device at the inventorylocation 712, such as a shelf controller, by the server 804, or byanother computing device.

At 1202, a first seed value 402(1) is accessed. In one implementation,the seed value 402 may be received from an external device such as aserver 804. The seed value 402 may be associated with a particulardevice based on the physical location within the facility 702. Forexample, the physical layout data 934 may comprise informationindicating that first shelf 102(1) is adjacent to second shelf 102(2),second shelf 102(2) is adjacent to third shelf 102(3), and so forth.Using this information, the seed value 402 may be allocated to thedevices in various inventory locations 712 to avoid two capacitivesensors 112(2) that are adjacent to one another from using the same seedvalue 402. In some implementations, the physical layout data 934 may beschematic or logical in nature, indicating the relative placement thatcorresponds to physical placement but without specific coordinate data,dimensional information, and so forth.

At 1204, first sequence data 408(1) comprising data elements indicativeof individual ones of the first plurality of capacitive sensors 112(2)is generated based on the first seed value 402(1). For example, thesequencer module 404 may utilize as input the first seed value 402(1)and generate first sequence data 408(1).

At 1206, a time delay value 406 is determined. For example, the timedelay value 406 may be determined based on output from a pseudorandomnumber generator.

At 1208, second sequence data 408(2) comprising data elements indicativeof individual ones of the first plurality of capacitive sensors 112(2)is generated. In one implementation, the second sequence data 408(2) maybe based on the first seed value 402(1). In another implementation, thesecond sequence data 408(2) may be based on a second seed value 402(2),or may be based on the first sequence data 408(1). The first sequencedata 408(1) differs from the second sequence data 408(2).

In one implementation, the generation of sequence data 408 may use apseudorandom function with the seed value 402 as an input. In anotherimplementation, the generation of the sequence data 408 may utilize hashfunction with the seed value 402 as an input. For example, the hashfunction may generate a hash output. The hash output may then beassociated with particular ones of the conductive elements 206. In someimplementations, the generation of sequence data 408 may use otherinputs in addition to the seed value 402. For example, the time dataprovided by an onboard clock, output from a random number generator, amedia access control address, and so forth, may be used in conjunctionwith the seed value 402 to generate sequence data 408.

The sequence data 408 may include a command or instruction indicative ofa pause that is based on the time delay value 406. For example, dataindicative of a 5 ms pause may be randomly inserted into the firstsequence data 408(1) by the sequencer module 404.

At 1210, the first plurality of capacitive sensors 112(2) are operatedat a first time in the order specified in the first sequence data408(1). When an instruction or command indicative of the pause isprocessed, operation of the capacitive sensor module 212 may betemporarily altered for the duration of the interval of time specifiedby the delay. For example, the switch module 210 may temporarilyincrease the amount of time spent on a particular conductive element206. In another example, the capacitive sensor module 212 may suspendthe generation of the capacitive signal 208 for the interval of time.

At 1212, the first plurality of capacitive sensors 112(2) are operatedat a second time in the order specified in the second sequence data408(2).

As described above, the seed values 402 and the resulting sequence data408 may differ between adjacent devices. For example, first shelf 102(1)with electronics including a capacitive sensor module 212 may use afirst seed value 402(1) while a second shelf 102(2) may use a secondseed value 402(2) that is different from the first seed value 402(1). Byoperating these adjacent capacitive sensors 112(2) using differentsequence data 408, interference between the two or more capacitivesensors 112(2) may be reduced or eliminated.

In some implementations, a particular set of sequence data 408 may beused more than once. For example, the first sequence data 408(1) and thesecond sequence data 408(2) may be alternately used.

FIG. 13 depicts a flow diagram of a process 1300 for using differentseed values 402 in adjacent inventory locations 712 to operateconductive elements 206 in a capacitive sensor 112(2), according to someimplementations. The process 1300 may be implemented at least in part byone or more of a computing device at the inventory location 712, such asa shelf controller, by the server 804, or by another computing device.

As depicted here, in some implementations, an external device such as aserver 804 may provide the seed values 402 to electronics operating thecapacitive sensors 112(2) for different shelves 102. A first shelf102(1) receives a first seed value 402(1) from the server 804. A secondshelf 102(2) receives a second seed value 402(2) from the server 804. Asdescribed above, the server 804 may be configured to distribute seedvalues 402 to avoid a situation where two adjacent devices are using thesame seed value 402.

In this illustration, time is shown as increasing down the page,indicated by the arrow 1302. At 1304, a first computing device operatingthe capacitive sensors 112(2) of the first shelf 102(1) receives thefirst seed value 402(1). At 1306, the first computing device generates afirst set of sequence data 408(1) based on the first seed value 402(1).At 1308, the first computing device operates the capacitive sensors112(2), or the conductive elements 206 thereof, in the order specifiedin the first sequence data 408(1).

At 1310, the second computing device operating the capacitive sensors112(2) of the second shelf 102(2) receives the second seed value 402(2).As illustrated here, the operations of 1310 through 1314 may occurcontemporaneously with those described with regard to 1304 through 1308.

At 1312, the second computing device generates a second set of sequencedata 408(2) based on the second seed value 402(2). At 1314, the secondcomputing device operates the capacitive sensors 112(2), or theconductive elements 206 thereof, in the order specified in the secondsequence data 408(2).

As a result of this technique, interference between capacitive sensors112(2) of the first shelf 102(1) and the second shelf 102(2) may beminimized.

FIG. 14 depicts a flow diagram of a process 1400 for using a conductiveelement 206 as part of a capacitive sensor 112(2) and as a radiofrequency antenna, according to some implementations. The process 1400may be implemented at least in part by one or more of a computing deviceat the inventory location 712, such as a shelf controller, by the server804, or by another computing device.

At 1402, a switch module 210 comprising switching circuitry operates toselectively connect particular ones of the one or more conductiveelements 206 to the circuitry of the capacitive sensor module 212 or toa radio module 508. In some implementations, the switch module 210 mayconnect to an antenna matching network module 512 instead of the radiomodule 508.

At 1404, an object is determined to be proximate to a conductive element206 of a capacitive sensor 112(2) based on output from the capacitivesensor module 212. For example, a change in capacitance values from afirst time to a second time that exceeds a threshold value may beindicative of proximity.

At 1406, the switching circuitry is operated to connect the one or moreparticular conductive elements 206 to the radio module 508 or theantenna matching network module 512. For example, a network of FETs orother transistors may be used to disconnect the particular conductiveelement 206 from the capacitive sensor module 212 and connect thatparticular conductive element 206 to a radio module 508.

At 1408, the radio module 508 may be operated to one or more of send orreceive a radio frequency signal using the one or more particularconductive elements 206 connected via the switch module 210. The one ormore particular conductive elements 206 are now acting as one or moreantennas. For example, the radio frequency signal 510, such as an RFIDinterrogation signal, may be generated by the radio module 508 andemitted by the conductive elements 206.

At 1410, switching circuitry of the switch module 210 may be operated todisconnect the radio module 508 or the antenna matching network module512 from the particular conductive element 206, and instead connect theconductive element 206 to the capacitive sensor module 212.

In some implementations, operation of the switch module 210 to connectthe radio module 508 or the antenna matching network module 512 toparticular conductive element 206 may be responsive to other event data506 or other data. For example, the change in weight values obtained bythe weight sensors 112(1) that exceeds a threshold value may result inthe switch module 210 coupling the radio module 508 to a particularconductive element 206.

By using this technique, the conductive element 206 may be used for morethan one purpose. Additionally, this technique when used in conjunctionwith arrays of the conductive elements 206 such as described above withregard to FIGS. 2 and 3 allows for the transmission and reception ofradio frequency signals 510 at particular physical locations. Thiscontrol may be used to determine the location of a radio frequencysource, such as determining the location of an RFID tag.

FIG. 15 depicts a flow diagram of a process 1500 for using capacitancedata 116 to select a particular configuration of an antenna matchingnetwork, according to some implementations. The process 1500 may beimplemented at least in part by one or more of a computing device at theinventory location 712, such as a shelf controller, by the server 804,or by another computing device. As described above, the impedance of anantenna may change based on the presence of nearby objects. Situationsin which the impedance of the antenna varies beyond a threshold amountfrom the impedance associated with the radio module 508 may result inpoor performance of the radio module 508 due to a poor transfer of powerbetween the radio module 508 and the antenna. Additionally, an impedancemismatch may result in the reflection of power emitted by the radiomodule 508 that may result in damage to the circuitry of the radiomodule 508, feedline, and so forth. By configuring an antenna matchingnetwork module 512 to address a potential impedance mismatch prior touse, performance of the radio module 508 during operation may beimproved and the likelihood of damage to the radio module 508 may bereduced.

At 1502, a capacitance value is determined using at least a portion of afirst plurality of capacitive sensors 112(1). For example, thecapacitance data 116 may indicate a particular capacitance valuereported by the capacitive sensor module 212.

At 1504, based on the capacitance value, a matching networkconfiguration 514 may be determined. For example, a lookup table orother data structure may be used to associate particular capacitancevalues with a particular matching network configuration 514.

At 1506, the antenna matching network module 512 is configured to usethe determined matching network configuration 514. For example, theantenna matching network module 512 may comprise circuitry that allowsfor reconfigurable circuit networks of particular inductors orcapacitors. In another example, the antenna matching network module 512may comprise discrete sections of circuitry, each providing a particularimpedance transform. Continuing this example, the antenna matchingnetwork module 512 may include switching circuitry that allows for theuse of a designated matching network configuration 514 to transfer theradio frequency signal 510.

At 1508, the radio module 508 coupled to the antenna matching networkmodule 512 is operated to one or more of send or receive a radiofrequency signal 510 using an antenna coupled to the antenna matchingnetwork module 512.

FIG. 16 depicts a flow diagram of a process 1600 for using a conductiveelement 206 as part of a capacitive sensor 112(1) and a radio frequencyantenna, with the tuning of that antenna being based on capacitance data116, according to some implementations. The process 1600 may beimplemented at least in part by one or more of a computing device at theinventory location 712, such as a shelf controller, by the server 804,or by another computing device.

At 1602, switching circuitry such as the switch module 210 is operatedto connect the particular ones of the one or more conductive elements206 to the capacitive sensing circuitry of the capacitive sensor module212.

At 1604, proximity of an object is determined based on output from thecapacitive sensor module 212.

At 1606, the switch module 210 is operated to connect the one or moreparticular conductive elements 206 to the antenna matching networkmodule 512.

At 1608, based on output from the capacitive sensor module 212, amatching network configuration 514 is determined. For example, based onthe capacitance data 116 generated by the capacitive sensor module 212,a lookup table may be used to retrieve the particular matching networkconfiguration 514 for use. In another example, the output from thecapacitive sensing circuitry may comprise proximity data 502. Responsiveto this proximity data 502, a particular matching network configuration514 may be determined.

At 1610, the antenna matching network module 512 is configured to usethe matching network configuration 514.

At 1612, the radio module 508 is operated to one or more of send orreceive a radio frequency signal 510 using the one or more particularconductive elements 206 as an antenna.

In other implementations, the order of this and the other flow diagramsin this disclosure may be changed. For example, the operations indicatedat 1608 and 1610 may occur prior to the operation of 1606.

FIG. 17 depicts a flow diagram of a process 1700 for using capacitancedata 116 to determine the weight data 114 from a weight sensor 112(1) atan inventory location 712, according to some implementations. Theprocess 1700 may be implemented at least in part by one or more of acomputing device at the inventory location 712, such as a shelfcontroller, by the server 804, or by another computing device.

At 1702, a first set of weight values is determined using one or more ofthe weight sensor 112(1). In some implementations, a single weight valuemay be determined.

At 1704, a baseline weight value is determined using the first set ofweight values. For example, the baseline weight value may be indicativeof a weight before the occurrence of an event. The baseline weight valuemay comprise a sum of weight values from several weight sensors 112(1),an average of weight values from several weight sensors 112(1), and soforth.

At 1706, a first capacitance value is determined using a capacitivesensor 112(2). The first capacitance value may comprise a baselinevalue. In some implementations, the baseline value may be determinedfrom one or more measurements made using a particular conductive element206 of the capacitive sensor 112(2).

At 1708, a second capacitance value is determined using the capacitivesensor 112(2). The second capacitance value may be determined from oneor more measurements made using a particular conductive element 206 ofthe capacitive sensor 112(2). For example, the second capacitance valuemay comprise an average of a plurality of individual capacitance values.

At 1710, a capacitance change value is determined. For example, thesecond capacitance value may be subtracted from the first capacitancevalue.

At 1712, the capacitance change value is determined to exceed athreshold value.

At 1714, a second set of weight values is determined using the one ormore weight sensors 112(1). The determination of the second set ofweight values may be responsive to the determination of 1712. In someimplementations, a single weight value may be determined.

At 1716, an event weight value is determined using the second set ofweight values. For example, the event weight value may be indicative ofa weight measured after the occurrence of the event.

At 1718, a weight change value is determined. For example, the weightchange value may be determined by subtracting the baseline weight valuefrom the event weight value.

At 1720, a first type of item 106 is determined that is associated withthe shelf 102 or other inventory location 712.

At 1722, item data 126 associated with the first type of item 106 isdetermined. As described above, the item data 126 may include one ormore of weight per item 106 or capacitance associated with item 106.

At 1724, a quantity change value is determined. The quantity changevalue is indicative of a change in quantity of the first type of item106 on the shelf 102 or inventory location 712. For example, thequantity change value may be determined by dividing the weight changevalue by the weight per item 106, dividing the capacitance change valueby the capacitance associated with the item 106, and so forth.

In some implementations, the capacitance data 116 may be used todifferentiate between different types of items 106. For example, thecapacitance value associated with a box of dried pasta may be differentfrom the capacitance value associated with a bottle filled with water.

The system may determine first item data 126(1) indicative of a changein capacitance associated with a first type of item 106. Continuing theprevious example, data obtained during intake processing of the driedpasta may indicate that an individual package of this type of item 106changes the capacitance at a capacitive sensor 112(2) by 82 microfarads.The system may then determine second item data 126(2) indicative of achange in capacitance associated a second type of item 106. For example,data obtained during intake processing of bottled water may indicatethat the bottle of water changes the capacitance of the capacitivesensor 112(2) by 347 microfarads. The system may generate firstcomparison data indicative of a first correspondence or comparisonbetween the capacitance change value and the first item data 126(1). Forexample, the capacitance change value may be 394 microfarads, and deemedto be within a threshold value of the first item data 126(1). The systemmay then generate second comparison data indicative of a secondcorrespondence or comparison between the capacitance change value andthe second item data 126(2). For example, the capacitance change valueof 394 microfarads may be deemed to be beyond the threshold value of thesecond item data 126(2). Based at least in part on these comparisons,the system may differentiate between a first type of item 106, such asthe dried pasta, that is associated with the shelf 102 and a second typeof item 106, such as the bottle of water, that is also associated withthe same shelf 102.

FIG. 18 depicts a flow diagram of a process 1800 for using capacitancedata 116 to generate event data 506 about an inventory location 712,according to some implementations. For example, a change in thecapacitance data 116 indicative of proximity of an object may be used todetermine a particular portion of the weight data 114. The process 1800may be implemented at least in part by one or more of a computing deviceat the inventory location 712, such as a shelf controller, by the server804, or by another computing device.

At 1802, a capacitance change value measured by a capacitive sensor112(2) at an inventory location 712 is determined. The capacitancechange value may be determined to exceed a threshold value. For example,a baseline capacitance value may be determined. The baseline capacitancevalue may calculated from a plurality of capacitance values obtainedwhen the sensor data from other sensors 112 is indicative of no otherevent or activity. The baseline capacitance value may be indicative of amoving average, minimum, maximum, and so forth. A threshold capacitancevalue may be determined that is based on the baseline capacitance value.For example, the threshold capacitance value may be set at 80% of themoving average.

At 1804, weight data 114 is determined using one or more the weightsensors 112(1). A weight change value measured by a weight sensor 112(1)at the inventory location 712 may then be determined.

At 1806, event data 506 indicative of occurrence of an event at theinventory location 712 is generated. For example, the event data 506 maybe indicative of presence of the user 108 at the inventory location 712or addition, removal, or movement of an item 106 at the inventorylocation 712. The event data 506 may be generated responsive to avariety of different conditions or combinations thereof. In a firstimplementation, the event data 506 may be indicative of the capacitancechange value exceeding a first threshold value and a weight change valueexceeding a second threshold value. In a second implementation, theevent data 506 may be indicative of the capacitance change valueexceeding a first threshold value and the weight change value being lessthan a second threshold value. For example, the capacitance change valuemay exceed the threshold capacitance value. In a third implementation,the event data 506 may be indicative of the capacitance change valuebeing less than a first threshold value and the weight change valueexceeding a second threshold value.

In some implementations, the filter function may be determined based onthe event data 506. For example, event data 506 indicative of a changein the capacitance value that exceeds a threshold value may result inthe data processing module 604 utilizing a particular filter function toprocess the sensor data associated with the event. Continuing theexample, a set of weight values corresponding to a time intervalassociated with the event data 504 obtained by the weight sensor 112(1)may be filtered using a specified filter function to generate processedweight data 608.

In some implementations, the event data 506 may be used to acquiresensor data, such as image data 118, associated with the inventorylocation 712. For example, after receipt of event data 506, an imagesensor 112(3) such as a camera may be determined that is associated withthe inventory location 712 as designated by the event data 506. Imagedata 118 associated with the inventory location 712 may then beobtained. In some implementations, the image data 118 may be obtained byretrieving previously stored image data 118 obtained by the image sensor112(3).

In some implementations, at 1808, generation of the interaction data 128may be responsive to the event data 506. For example, the event data 506may trigger analysis of the processed sensor data by the interactiondata module 616 to generate the interaction data 128.

The system may determine the interaction data 128 based at least in parton the event data 506. For example, the event data 506 may be used totrigger the determination of the interaction data 128, select aparticular portion of sensor data for processing and analysis, and soforth. First item data 126(1) associated with a first type of item 106is determined. Second item data 126(2) associated with a second type ofitem 106 is determined. Interaction data 128 indicative of aninteraction with one or more of the first type of item 106 associatedwith the inventory location 712 or the second type of item 106associated with the inventory location 712 is determined based at leastin part on the capacitance change value. For example, the type of item106 that has item data 126 that has been determined to have capacitivecharacteristics that are closest in value to the capacitance changevalue may be deemed to be the type of item 106 associated with theinteraction.

The interaction data 128 may be based at least in part on the comparisonof previously stored weight data with weight change data associated withan inventory location 712 at a particular time. For example, responsiveto the event data 506, a change in quantity of the first type of item106 at the inventory location 712 may be determined by dividing theweight change value by the previously stored weight data of a knownquantity of items.

The system may determine an interval of time associated with the eventdata 506. For example, the interval of time may extend from one secondbefore to one second after the time of the event. A set of weight data114 obtained by the weight sensor 112(1) during the interval of time maybe determined. A weight change value may then be determined using theset of weight data 114.

Embodiments may be provided as a software program or computer programproduct including a non-transitory computer-readable storage mediumhaving stored thereon instructions (in compressed or uncompressed form)that may be used to program a computer (or other electronic device) toperform processes or methods described herein. The computer-readablestorage medium may be one or more of an electronic storage medium, amagnetic storage medium, an optical storage medium, a quantum storagemedium, and so forth. For example, the computer-readable storage mediamay include, but is not limited to, hard drives, floppy diskettes,optical disks, read-only memories (ROMs), random access memories (RAMs),erasable programmable ROMs (EPROMs), electrically erasable programmableROMs (EEPROMs), flash memory, magnetic or optical cards, solid-statememory devices, or other types of physical media suitable for storingelectronic instructions. Further, embodiments may also be provided as acomputer program product including a transitory machine-readable signal(in compressed or uncompressed form). Examples of transitorymachine-readable signals, whether modulated using a carrier orunmodulated, include, but are not limited to, signals that a computersystem or machine hosting or running a computer program can beconfigured to access, including signals transferred by one or morenetworks. For example, the transitory machine-readable signal maycomprise transmission of software by the Internet. Separate instances ofthese programs can be executed on or distributed across any number ofseparate computer systems. Thus, although certain steps have beendescribed as being performed by certain devices, software programs,processes, or entities, this need not be the case, and a variety ofalternative implementations will be understood by those having ordinaryskill in the art.

Additionally, those having ordinary skill in the art will readilyrecognize that the techniques described above can be utilized in avariety of devices, environments, and situations. Although the subjectmatter has been described in language specific to structural features ormethodological acts, it is to be understood that the subject matterdefined in the appended claims is not necessarily limited to thespecific features or acts described. Rather, the specific features andacts are disclosed as illustrative forms of implementing the claims.

What is claimed is:
 1. A method comprising: determining a first weightvalue using a weight sensor, the first weight value indicative of aweight value before occurrence of an event; determining a firstcapacitance value using a capacitive sensor; determining a secondcapacitance value using the capacitive sensor; determining a capacitancechange value by subtracting the second capacitance value from the firstcapacitance value; determining the occurrence of the event based on thecapacitance change value exceeding a threshold value; responsive to thecapacitance change value exceeding the threshold value, determining asecond weight value using the weight sensor, the second weight valueindicative of a weight value after the occurrence of the event; anddetermining a weight change value by subtracting the first weight valuefrom the second weight value.
 2. The method of claim 1, furthercomprising: determining a first type of item associated with a shelf;determining item data associated with the first type of item, whereinthe item data includes one or more of weight per item or capacitanceassociated with the first type of item; and determining a quantitychange value indicative of a change in quantity of the first type ofitem on the shelf by one or more of: division of the weight change valueby the weight per item, or division of the capacitance change value bythe capacitance associated with the first type of item.
 3. The method ofclaim 1, further comprising: accessing previously stored first item dataindicative of a change in capacitance associated with a first sampleitem representative of a first type of item; generating first comparisondata indicative of a correspondence between the capacitance change valueand the previously stored first item data; determining thecorrespondence is within a second threshold value; and determining atype of item associated with the weight change value based at least inpart on the correspondence being within the second threshold value.
 4. Amethod comprising: determining a capacitance change value measured by acapacitive sensor at an inventory location; determining a weight changevalue measured by a weight sensor at the inventory location; andgenerating event data indicative of occurrence of an event at theinventory location based on the capacitance change value and the weightchange value.
 5. The method of claim 4, further comprising: determiningfirst item data associated with a first type of item; determining seconditem data associated with a second type of item; and determininginteraction data indicative of an interaction with one or more of thefirst type of item associated with the inventory location or the secondtype of item associated with the inventory location based at least inpart on the capacitance change value.
 6. The method of claim 4, furthercomprising: determining a calculated weight of a predetermined quantityof a first type of item stowed at the inventory location; anddetermining a change in quantity of the first type of item at theinventory location using the weight change value and the calculatedweight.
 7. The method of claim 4, wherein the event data is indicativeof the capacitance change value exceeding a first threshold value andthe weight change value exceeding a second threshold value.
 8. Themethod of claim 4, further comprising: determining the capacitancechange value exceeds a first threshold value; and determining the weightchange value is less than a second threshold value.
 9. The method ofclaim 4, further comprising: determining the capacitance change value isless than a first threshold value; and determining the weight changevalue exceeds a second threshold value.
 10. The method of claim 4,further comprising: based on the capacitance change value exceeding athreshold value, determining a filter function; processing with thefilter function one or more weight values obtained by the weight sensorto produce a set of filtered weight values; and wherein the determiningthe weight change value uses the set of filtered weight values.
 11. Themethod of claim 4, further comprising: determining an image sensorassociated with the inventory location; and responsive to the eventdata, obtaining image data using the image sensor associated with theinventory location.
 12. The method of claim 4, further comprising:acquiring a first set of weight data acquired from the weight sensorover a period of time prior to a time indicated by the event data;generating a noise profile characterizing a first set of weight valuesfrom the weight sensor prior to the occurrence of the event, wherein thecharacterizing uses one or more functions; acquiring a second set ofweight values output from the weight sensor contemporaneous to a timeindicated by the event data; generating weight data by subtracting thenoise profile from the second set of weight values; and determining theweight change value using the weight data.
 13. The method of claim 4,further comprising: determining an interval of time associated with theevent data; determining a set of weight data obtained by the weightsensor during the interval of time; and determining the weight changevalue using the set of weight data.
 14. A method comprising: determininga first set of capacitance values measured by a capacitive sensor at aninventory location; determining a first set of weight values measured bya weight sensor at the inventory location; and generating event dataindicative of an occurrence of an event at the inventory location basedat least in part on one or more of the first set of capacitance valuesor the first set of weight values.
 15. The method of claim 14, furthercomprising: determining a baseline capacitance value; determining athreshold capacitance value based on the baseline capacitance value; andwherein the event data is based at least in part on determining at leasta portion of the first set of capacitance values exceeds the baselinecapacitance value.
 16. The method of claim 14, further comprising:determining at least a portion of the first set of capacitance valuesexceeds a first threshold value; determining at least a portion of thefirst set of weight values exceeds a second threshold value; andinitiating the generating of the event data indicative of the occurrenceof the event at the inventory location.
 17. The method of claim 14,further comprising: determining first item data associated with a firsttype of item; determining a change in quantity of the first type of itemassociated with the inventory location based at least in part on one ormore of the first set of capacitance values or the first set of weightvalues; and wherein the event data is indicative of the change inquantity of the first type of item at the inventory location.
 18. Themethod of claim 14, further comprising: determining a baseline weightvalue using at least a portion of the first set of weight valuesobtained at a first time; determining an event weight value using atleast a portion of the first set of weight values obtained at a secondtime; determining a weight change value as a difference between thebaseline weight value and the event weight value; and wherein the eventdata is indicative of a change in quantity of a first type of item atthe inventory location.
 19. The method of claim 14, further comprising:determining an interval of time associated with the event data;determining a subset of the first set of weight values that wereobtained by the weight sensor during the interval of time; determining aweight change value using the subset of the first set of weight values;determining a change in quantity of a first type of item at theinventory location; and wherein the event data is indicative of thechange in quantity of the first type of item.
 20. The method of claim14, further comprising: delivering an electric potential to a shieldproximate to a conductive element of the capacitive sensor, the electricpotential to produce a voltage difference between a conductive elementand the shield that is less than a threshold voltage.